DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/20/2026 is being considered by the examiner.
Response to Amendment
Applicant’s remarks, filed 07/20/2026, regarding the objections made to the claims submitted in the non-final office action dated 04/22/2026 are withdrawn due to the amendments made to the claims.
Response to Arguments
Applicant's arguments see remarks, filed 07/20/2026, with respect to the claims 16-25 and 31-35 have been fully considered but are not persuasive.
The applicant argues on page 7, “Applicant respectfully traverses the obviousness rejection over the combination of Kim, Guermoud, and Guermoud2 on the grounds that the combination lacks a permissible combination to combine and also fails to present evidence of obviousness for all limitations. Either ground is sufficient to demonstrate that no prima facie evidence of obviousness was established. First, Applicant respectfully submits that the Office has plucked isolated components from three different video-processing architectures: a. A real-time mathematical offset calculator (Kim); b. A temporal shot-level luminance clamper (Guermoud); and C. An offline-trained, closest-match cluster template matcher (Guermoud2). A proper motivation (e.g., one having a reasoned explanation with a sound technical basis to support the combination) must be provided. A person of ordinary skill would consider these to be incompatible systems. At the least, reasoning is required to explain how to combine these distinct systems. Instead, the Office appears to allege they are all tone mapping disclosures with unmet needs:”
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the office have clearly described that it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the teachings of KIM et al. (US 20220277428 A1), in view of GUERMOUD et al. (US 20180115742 A1), which both perform a function of tone mapping on dynamic range videos, with the teachings of GUERMOUD et al. (US 20170337670 A1), which also performs a tone mapping function on dynamic range videos. The identified unmet needs of KIM et al. (US 20220277428 A1), in view of GUERMOUD et al. (US 20180115742 A1), and further in view of GUERMOUD et al. (US 20170337670 A1) are identified unmet needs for which their inventions resolve, and thus is serves as the teaching, suggestion, or motivation to combine the references. Wherein having KIM’s method of tone mapping determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD the brightness of the bright video shots having a short duration is reduced in the HDR sequence. The discomfort perceived by the viewer is thus reduced. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20180115742 A1), Paragraph [0042].
Wherein having KIM’s method of tone mapping applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD2 relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD2 there is a need to set a global tone mapping algorithm that can adapt automatically to the content to tone-map. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20170337670 A1), Paragraph [0011].
The applicant argues on page 7, “Office Action at page 11. The combination must be operable, and as will be shown, it is not.” Further on Page 8, the applicant argues “Applicant notes that Kim does not disclose or suggest a plurality (e.g., a database or a library) of pre-defined profiles. Instead, KIM dynamically calculates a single mathematical offset gain to apply to a single, pre-existing look-up table (LUT) curve to prevent luminance saturation. Id. at para. [0190], [0198]. Accordingly, Applicant objects to the interpretation that: identifying a second profile of the plurality of profiles (Fig. 11, illustrates a second profile (i.e. the line called OFFSET APPLIED. Paragraph [0196]-KIM discloses "offset applied" indicates an LUT curve when the offset gain is applied.) corresponding to a first determined intermediate state Office Action at page 6. This is also inconsistent with the Office admission that KIM in view of GUERMOUD fail to explicitly teach applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function. Office Action at page 9. Applying a dynamic mathematical offset to a single curve is fundamentally different from mapping a determined state value to a dedicated profile selected from a plurality of distinct profiles, where each profile is associated with its own pre-defined expansion function. Because Kim lacks a plurality of profiles corresponding to state values in the first place, it cannot be modified to teach or suggest applying an inverse tone mapping using an expansion function determined based on profiles and transitions between state values. No prima facie evidence of obviousness was presented for the limitations.”
In response, the Office does not find this argument to be persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a plurality (e.g., a database or a library) of pre-defined profiles) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Based on the breadth of the claim language, the prior art KIM et al. (US 20220277428 A1) explicitly teaches identifying a second profile of the plurality of profiles (Fig. 11, illustrates a second profile (i.e. the line called OFFSET APPLIED. Paragraph [0196]-KIM discloses “offset applied” indicates an LUT curve when the offset gain is applied. For clarity on the record, the Office understands the LUT curves illustrated in Fig. 11 to be different profiles.) corresponding to a first determined intermediate state value (Fig. 1. Paragraph [0160]-KIM discloses the controller 170 may obtain an offset point in order to differently apply the offset gain depending on a region in which data is concentrated. Further in paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).), and
The applicant argues on page 10, “Office Action at page 6. Applicant respectfully submits that Guermoud's temporal adjustment of bright video shots to prevent eye strain (id. at para. [0039], [0057]) is not evidence of the claimed transition control. Guermoud discloses detecting if a SDR sequence comprises a sub-sequence of at least two successive short video shots, at least one being a bright video shot. A short video shot designates a shot having a duration equal to or lower than a short duration threshold d1 (e.g., 2 seconds). A bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e., surface) greater than a size threshold SZ1. When a short, bright shot is detected, Guermoud simply clamps the peak luminance to prevent visual discomfort (e.g., when a shot transition is too abrupt). Guermoud has no disclosure or suggestion of extracting frame-level state values based on the bins of the histogram of SDR frames, evaluating frame-by- frame state transitions against a predefined set of allowed transitions, nor determining and inserting an intermediate state value to select a non-closest, transition-smoothing profile - Guermoud does not regulate profile switches using the claimed state architecture. No prima facie evidence of obviousness was presented for the limitation.”
In response, the Office does not find this argument to be persuasive. Based on the breadth of the claim language, the prior art GUERMOUD et al. (US 20180115742 A1) explicitly teaches determining that a transition between a previous state value and the first state value (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein L1 is a previous state value and the measured luminance that is compared to L1 is the first state value).) belongs to a set of allowed transitions (Fig. 3. Paragraph [0057]-GUERMOUD discloses the block 111 detects if the video shots are bright video shots or non-bright video shots. As mentioned above, a bright video shot is a video shot comprising at least one key frame including a bright area having a luminance value greater than L1 and a size greater than SZ1. The key frame is for example the first frame of the video shot. The block 111 delivers a signal F3 having a high level when a bright video shot is detected (wherein belonging to a set of allowed transitions is when a bright video shot is detected). For clarity on the record, the Office understands the transition to occur when threshold determines that a bright shot has been detected and a clamp decision is made based on the results of the detection.), the previous state value being representative of a previous SDR picture preceding the current SDR picture (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein L1 has been determined based on testing previous images).);-
responsive to the transition is belonging to the set of allowed transitions (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein exceeding luminance and size thresholds are allowed transitions). For clarity on the record, the Office understands the transition to occur when threshold determines that a bright shot has been detected and a clamp decision is made based on the results of the detection.),
responsive to the transition not belonging to the set of allowed transitions (Fig. 3. Paragraph [0039]-GUERMOUD discloses since only large areas with a high number of pixels having high luminance values will generate high values at the output of such filters, at the output of this filter any value higher than the luminance threshold L1 will be considered as indicating a bright area (wherein any value lower than L1 will not be considered and is thus not belonging to the allowed transitions). For clarity on the record, the Office understands the transition to occur when threshold determines that a bright shot has been detected and a clamp decision is made based on the results of the detection.),
applying an inverse tone mapping (Fig. 3, #12 called inverse tone mapping. Paragraph [0050]-GUERMOUD discloses a mapper 12 for applying inverse tone mapping to the SDR as a function of the results of the detector 11.) to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture (Fig. 3, illustrates applying inverse tone mapping to obtain an HDR image. Paragraph [0040]-GUERMOUD discloses in a step S3, an adaptive inverse tone mapping is applied to the SDR sequence. According to the invention, the luminance range of bright video shots of the detected subsequence is mapped from the low dynamic range to a first high dynamic range while the other shots of the SDR sequence from the standard dynamic range to a second, high dynamic range, the first high dynamic range having a maximal value Lmax1 lower than the maximal value Lmax2 of the second high dynamic range (wherein the map is the expansion function and the second high dynamic range is a second identified profile).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM of a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD of determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
Wherein having KIM’s method of tone mapping determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD the brightness of the bright video shots having a short duration is reduced in the HDR sequence. The discomfort perceived by the viewer is thus reduced. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20180115742 A1), Paragraph [0042].
The applicant argues on pages 9-10, “The Office relies on Guermoud2 to teach a plurality of profiles (Office Action at page 9), each profile of the plurality of profiles being associated to an expansion function (Office Action at page 10). However, the rejection is improper as the references teach away from combination. Applicant notes that Guermoud2's objective is to compare an input frame's visual feature (for instance a histogram of the input frame) to cluster templates and select the closest matching cluster to apply its associated expansion function, thereby preserving the artistic intent of the colorist. Id. at para. [0018], [0040], [0088]. In contrast, under the claims, if a transition between state values is not allowed, the system is forced to determine an intermediate state value and apply the expansion function of a profile associated with that intermediate state value. Stated differently, the system deliberately applies an expansion function that is not the closest match to the current frame's actual content to achieve temporal smoothing. Applicant realizes Kim is cited for allegedly disclosing the intermediate state value, but the combination nonetheless requires modifying Guermoud2 to select an intermediate profile instead of the closest matching profile, thereby teaching away from its core design principle, and thus the proposed modification would render the prior art unsatisfactory for its intended purpose. Selecting a non- closest intermediate profile directly defeats Guermoud2's objective of precise cluster matching, and seems to be evidence of an impermissible hindsight being used to combine the references in spite of their teachings away from combination, and certainly fails to teach the obviousness of the limitation it was cited against.”
In response to the, the Office does not find the argument to be persuasive. Based on the breadth of the claim language and as stated above, the prior art KIM et al. (US 20220277428 A1) explicitly teaches identifying a second profile of the plurality of profiles (Fig. 11, illustrates a second profile (i.e. the line called OFFSET APPLIED. Paragraph [0196]-KIM discloses “offset applied” indicates an LUT curve when the offset gain is applied. For clarity on the record, the Office understands the LUT curves illustrated in Fig. 11 to be different profiles.) corresponding to a first determined intermediate state value (Fig. 1. Paragraph [0160]-KIM discloses the controller 170 may obtain an offset point in order to differently apply the offset gain depending on a region in which data is concentrated. Further in paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).), and. Moreover, the Office employs the prior art GUERMOUD et al. (US 20170337670 A1) to teach applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function. The prior art GUERMOUD et al. (US 20170337670 A1) explicitly teaches applying an inverse tone mapping (Fig. 3, #4 called inverse tone mapper. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.) to the current SDR picture using an expansion function (Fig. 1. Paragraph [0072]-GUERMOUD2 discloses these functions are each defined by 3 parameters: a, b and c, which means 30 parameters a, 30 parameters b and 30 parameters c for one cluster. These parameters are sorted in ascending or descending order and the median value for each parameter (a.sub.median, b.sub.median, c.sub.median) is selected as the parameters defining a function representing an expansion exponent map for a cluster. These parameters form “expansion exponent map data” of this cluster (wherein an expansion function is expansion exponent map data).) associated with a first profile of a plurality of profiles (Fig. 2. Paragraph [0073]-GUERMOUD2 discloses in the same manner, in the step S2, a visual feature is computed for each cluster based on the 30 visual features of the reference images of the cluster. This visual feature of a reference image is a feature representative of the luminance of the different reference images of the cluster. The visual feature of a reference image is for example the histogram of the luminance levels of the pixels of said reference image or image (wherein a histogram is a profile).) corresponding to the first state value (Fig. 1-2. Paragraph [0064]-GUERMOUD2 discloses a single expansion exponent map is computed for each cluster based on the reference expansion exponent maps of the reference images of the cluster. This step is illustrated by FIG. 2. In this figure, there are 3 clusters, each comprising 30 reference images. A function representing an expansion exponent map is generated, for each cluster, based on the 30 reference expansion exponent maps of the reference images of the cluster. Paragraph [0087-0088]-GUERMOUD2 discloses the visual feature of the image I is compared with the visual features of the clusters of reference images according to a distance criterion. For example, a Euclidean distance between the parameters of the visual feature of the image I and the parameters of the visual feature of each one of the clusters. In the step S5, the cluster, the visual feature of which is the closest (minimum distance) to the visual feature of the image I is selected (wherein a first state value is an expansion exponent map for a cluster, as shown in fig. 2).) to obtain a high dynamic range (HDR) picture (Fig. 3, illustrates the outputting of an expanded image (i.e. an HDR picture) from the inverse tone mapper #4. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.),
each profile of the plurality of profiles being associated to an expansion function (Fig. 2. Paragraph [0073]-GUERMOUD2 discloses in the same manner, in the step S2, a visual feature is computed for each cluster based on the 30 visual features of the reference images of the cluster. This visual feature of a reference image is a feature representative of the luminance of the different reference images of the cluster. The visual feature of a reference image is for example the histogram of the luminance levels of the pixels of said reference image or image. Further in paragraph [0092]-GUERMOUD discloses the memory 1 stores, for each one of the clusters of reference images, expansion exponent map data and a visual feature, representative of the luminance of reference images of the cluster.); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD of a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD2 of applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
Wherein having KIM’s method of tone mapping applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD2 relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD2 there is a need to set a global tone mapping algorithm that can adapt automatically to the content to tone-map. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20170337670 A1), Paragraph [0011].
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 16, 18-21, 23-25, 31, and 33-35 are rejected under 35 U.S.C. 103 as being unpatentable over KIM et al. (US 20220277428 A1), hereinafter referenced as KIM, in view of GUERMOUD et al. (US 20180115742 A1), hereinafter referenced as GUERMOUD, and further in view of GUERMOUD et al. (US 20170337670 A1), hereinafter referenced as GUERMOUD2.
Regarding claim 16, KIM explicitly teaches a method comprising (Fig. 6, illustrates a method of tone mapping. Paragraph [0129]):
obtaining a first histogram (Fig. 9, illustrates a first histogram. Paragraph [0162].) of a current standard dynamic range (SDR) picture (Fig. 8, S102 is for acquire histogram of input image. Paragraph [0147]-KIM discloses the controller 170 may acquire a histogram of an input image (S10[2]) (wherein an input image an SDR picture).), the first histogram comprising a first number of bins (Fig. 9, illustrates a histogram comprising a first number of bins. Paragraph [0147]-KIM discloses the controller 170 may acquire a histogram of an input image (S10[2]) (wherein an input image an SDR picture).), a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture (Fig. 9, illustrates bins associated with luminance levels and their associated number of occurrences. [0148-0149] KIM discloses the histogram of the input image may be a graph showing distribution of a luminance level of an input image and shows a frequency for each luminance level of the input image. For example, the controller 170 may group luminance levels of the input image in units of 32 bins (wherein luminance is a sample value).);
determining a first most representative bin (Fig. 9, illustrates #21 is the max bin for the first histogram. Paragraph [0165]-KIM discloses the controller 170 may acquire the max bin, and in the example of FIG. 9, the max bin may be a bin #21.) representative of the current SDR picture from the first histogram (Fig. 8, illustrates S102 called acquire histogram of input image (wherein the input image is an SDR picture). Paragraph [0154-0155]-KIM discloses the controller 170 may acquire the max bin and a representative bin belonging to a group with a high ratio of data (S106). The max bin may refer to a bin with the highest frequency among 32 bins. That is, the max bin may be a bin with concentrated data in the histogram of the input image (wherein the most representative bin is the max bin).), the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture (Fig. 9. Paragraph [0155]-KIM discloses the max bin may refer to a bin with the highest frequency among 32 bins. That is, the max bin may be a bin with concentrated data in the histogram of the input image (wherein the input image is an SDR picture).);
determining a first state value (Fig. 9, illustrates bins associated with luminance levels and their associated number of occurrences. [0148-0149] KIM discloses the histogram of the input image may be a graph showing distribution of a luminance level of an input image and shows a frequency for each luminance level of the input image. For example, the controller 170 may group luminance levels of the input image in units of 32 bins (wherein luminance level is a first state value).) based on the determined first representative bin (Fig. 8. Paragraph [0154-0156]-KIM discloses the controller 170 may acquire the max bin and a representative bin belonging to a group with a high ratio of data (S106). The max bin may refer to a bin with the highest frequency among 32 bins. That is, the max bin may be a bin with concentrated data in the histogram of the input image. For example, the controller 170 may acquire a bin with the highest frequency as the max bin in the histogram (wherein the luminance level of the max bin is a first state value).);
determining at least one intermediate state value (Fig. 1. Paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).) from the set of allowed transitions (Fig. 1. Paragraph [0167-0169]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio. In the example of FIG. 9, the controller 170 may determine whether a bin #21 as the max bin corresponds to any one of bins #17 to #22 as the group G6 with a high data ratio. When the max bin belongs to the group with a high data ratio, the controller 170 may acquire the max bin as an offset point. In the example of FIG. 9, the controller 170 may acquire a bin #21 as an offset point. When the max bin does not belong to the group with a high data ratio, the controller 170 may acquire a bin corresponding to an average of the max bin and the representative bin as an offset point (wherein an offset corresponding to the group with a high data ratio is an allowed transition).),
identifying a second profile of the plurality of profiles (Fig. 11, illustrates a second profile (i.e. the line called OFFSET APPLIED. Paragraph [0196]-KIM discloses “offset applied” indicates an LUT curve when the offset gain is applied.) corresponding to a first determined intermediate state value (Fig. 1. Paragraph [0160]-KIM discloses the controller 170 may obtain an offset point in order to differently apply the offset gain depending on a region in which data is concentrated. Further in paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).), and
KIM fails to explicitly teach determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
However, GUERMOUD explicitly teaches determining that a transition between a previous state value and the first state value (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein L1 is a previous state value and the measured luminance that is compared to L1 is the first state value).) belongs to a set of allowed transitions (Fig. 3. Paragraph [0057]-GUERMOUD discloses the block 111 detects if the video shots are bright video shots or non-bright video shots. As mentioned above, a bright video shot is a video shot comprising at least one key frame including a bright area having a luminance value greater than L1 and a size greater than SZ1. The key frame is for example the first frame of the video shot. The block 111 delivers a signal F3 having a high level when a bright video shot is detected (wherein belonging to a set of allowed transitions is when a bright video shot is detected).), the previous state value being representative of a previous SDR picture preceding the current SDR picture (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein L1 has been determined based on testing previous images).);-
responsive to the transition is belonging to the set of allowed transitions (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein exceeding luminance and size thresholds are allowed transitions).),
responsive to the transition not belonging to the set of allowed transitions (Fig. 3. Paragraph [0039]-GUERMOUD discloses since only large areas with a high number of pixels having high luminance values will generate high values at the output of such filters, at the output of this filter any value higher than the luminance threshold L1 will be considered as indicating a bright area (wherein any value lower than L1 will not be considered and is thus not belonging to the allowed transitions).),
applying an inverse tone mapping (Fig. 3, #12 called inverse tone mapping. Paragraph [0050]-GUERMOUD discloses a mapper 12 for applying inverse tone mapping to the SDR as a function of the results of the detector 11.) to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture (Fig. 3, illustrates applying inverse tone mapping to obtain an HDR image. Paragraph [0040]-GUERMOUD discloses in a step S3, an adaptive inverse tone mapping is applied to the SDR sequence. According to the invention, the luminance range of bright video shots of the detected subsequence is mapped from the low dynamic range to a first high dynamic range while the other shots of the SDR sequence from the standard dynamic range to a second, high dynamic range, the first high dynamic range having a maximal value Lmax1 lower than the maximal value Lmax2 of the second high dynamic range (wherein the map is the expansion function and the second high dynamic range is a second identified profile).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM of a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD of determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
Wherein having KIM’s method of tone mapping determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD the brightness of the bright video shots having a short duration is reduced in the HDR sequence. The discomfort perceived by the viewer is thus reduced. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20180115742 A1), Paragraph [0042].
KIM in view of GUERMOUD fail to explicitly teach applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
However, GUERMOUD2 explicitly teaches applying an inverse tone mapping (Fig. 3, #4 called inverse tone mapper. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.) to the current SDR picture using an expansion function (Fig. 1. Paragraph [0072]-GUERMOUD2 discloses these functions are each defined by 3 parameters: a, b and c, which means 30 parameters a, 30 parameters b and 30 parameters c for one cluster. These parameters are sorted in ascending or descending order and the median value for each parameter (a.sub.median, b.sub.median, c.sub.median) is selected as the parameters defining a function representing an expansion exponent map for a cluster. These parameters form “expansion exponent map data” of this cluster (wherein an expansion function is expansion exponent map data).) associated with a first profile of a plurality of profiles (Fig. 2. Paragraph [0073]-GUERMOUD2 discloses in the same manner, in the step S2, a visual feature is computed for each cluster based on the 30 visual features of the reference images of the cluster. This visual feature of a reference image is a feature representative of the luminance of the different reference images of the cluster. The visual feature of a reference image is for example the histogram of the luminance levels of the pixels of said reference image or image (wherein a histogram is a profile).) corresponding to the first state value (Fig. 1-2. Paragraph [0064]-GUERMOUD2 discloses a single expansion exponent map is computed for each cluster based on the reference expansion exponent maps of the reference images of the cluster. This step is illustrated by FIG. 2. In this figure, there are 3 clusters, each comprising 30 reference images. A function representing an expansion exponent map is generated, for each cluster, based on the 30 reference expansion exponent maps of the reference images of the cluster. Paragraph [0087-0088]-GUERMOUD2 discloses the visual feature of the image I is compared with the visual features of the clusters of reference images according to a distance criterion. For example, a Euclidean distance between the parameters of the visual feature of the image I and the parameters of the visual feature of each one of the clusters. In the step S5, the cluster, the visual feature of which is the closest (minimum distance) to the visual feature of the image I is selected (wherein a first state value is an expansion exponent map for a cluster, as shown in fig. 2).) to obtain a high dynamic range (HDR) picture (Fig. 3, illustrates the outputting of an expanded image (i.e. an HDR picture) from the inverse tone mapper #4. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.),
each profile of the plurality of profiles being associated to an expansion function (Fig. 2. Paragraph [0073]-GUERMOUD2 discloses in the same manner, in the step S2, a visual feature is computed for each cluster based on the 30 visual features of the reference images of the cluster. This visual feature of a reference image is a feature representative of the luminance of the different reference images of the cluster. The visual feature of a reference image is for example the histogram of the luminance levels of the pixels of said reference image or image. Further in paragraph [0092]-GUERMOUD discloses the memory 1 stores, for each one of the clusters of reference images, expansion exponent map data and a visual feature, representative of the luminance of reference images of the cluster.); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD of a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD2 of applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
Wherein having KIM’s method of tone mapping applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD2 relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD2 there is a need to set a global tone mapping algorithm that can adapt automatically to the content to tone-map. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20170337670 A1), Paragraph [0011].
Regarding claim 18, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the method of claim 16,
KIM further explicitly teaches wherein at least one second most representative bin is determined from the first histogram (Fig. 9, illustrates a histogram comprising a first number of bins. Paragraph [0147]-KIM discloses the controller 170 may acquire a histogram of an input image (S10[2]) (wherein an input image an SDR picture).), a second most representative bin being a bin of the first histogram different from the most representative bin representing one of the highest number of samples of the current SDR picture (Fig. 9, illustrates various bins with max bin, #21, and any bin in G6 representing a bin eligible to be the second most representative bin. Paragraph [0156-0157]-KIM discloses the controller 170 may acquire a bin with the highest frequency as the max bin in the histogram. The representative bin may be any one bin belonging to a group with a high ratio of data. For example, the representative bin may be the first bin of the group with a high data ratio (wherein the representative bin is the second most representative bin).), each determined most representative bin being used to identify determine the first state value (Fig. 9. Paragraph [0148-0149]-KIM discloses the histogram of the input image may be a graph showing distribution of a luminance level of an input image and shows a frequency for each luminance level of the input image. For example, the controller 170 may group luminance levels of the input image in units of 32 bins (wherein the luminance level of a representative bin is the first state value).).
Regarding claim 19, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the method of claim 16,
KIM further explicitly teaches comprising, responsive to at least one second intermediate state value (Fig. 1. Paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).) is determined from the set of allowed transitions (Fig. 1. Paragraph [0167-0169]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio. In the example of FIG. 9, the controller 170 may determine whether a bin #21 as the max bin corresponds to any one of bins #17 to #22 as the group G6 with a high data ratio. When the max bin belongs to the group with a high data ratio, the controller 170 may acquire the max bin as an offset point. In the example of FIG. 9, the controller 170 may acquire a bin #21 as an offset point. When the max bin does not belong to the group with a high data ratio, the controller 170 may acquire a bin corresponding to an average of the max bin and the representative bin as an offset point (wherein an offset corresponding to the group with a high data ratio is an allowed transition).),
KIM in view of GUERMOUD fail to explicitly teach identifying a third profile in the plurality of profiles for each second determined intermediate state value, and applying an inverse tone mapping to SDR pictures following the current SDR picture using expansion functions associated to each identified third profiles to obtain a corresponding HDR picture.
However, GUERMOUD2 explicitly teaches identifying a third profile in the plurality of profiles (Fig. 2, illustrates 3 profiles (wherein the histograms are profiles and the reference images are intermediate state values). Paragraph [0064]) for each second determined intermediate state value (Fig. 2. Paragraph [0064]-GUERMOUD discloses a single expansion exponent map is computed for each cluster based on the reference expansion exponent maps of the reference images of the cluster. This step is illustrated by FIG. 2. In this figure, there are 3 clusters, each comprising 30 reference images. A function representing an expansion exponent map is generated, for each cluster, based on the 30 reference expansion exponent maps of the reference images of the cluster (wherein an intermediate state value is a reference expansion exponent map for an image).), and applying an inverse tone mapping (Fig. 3, #4 called inverse tone mapper. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.) to SDR pictures following the current SDR picture using expansion functions (Fig. 2. Paragraph [0072]-GUERMOUD2 discloses these functions are each defined by 3 parameters: a, b and c, which means 30 parameters a, 30 parameters b and 30 parameters c for one cluster. These parameters are sorted in ascending or descending order and the median value for each parameter (a.sub.median, b.sub.median, c.sub.median) is selected as the parameters defining a function representing an expansion exponent map for a cluster. These parameters form “expansion exponent map data” of this cluster (wherein an expansion function is expansion exponent map data).) associated to each identified third profiles (Fig. 2, illustrates expansion maps for the three identified profiles. Paragraph [0064]-GUERMOUD2 discloses in this figure, there are 3 clusters, each comprising 30 reference images. A function representing an expansion exponent map is generated, for each cluster, based on the 30 reference expansion exponent maps of the reference images of the cluster.) to obtain a corresponding HDR picture (Fig. 3, illustrates the outputting of an expanded image (i.e. an HDR picture) from the inverse tone mapper #4. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD and further in view of GUERMOUD2 of a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD2 of identifying a third profile in the plurality of profiles for each second determined intermediate state value, and applying an inverse tone mapping to SDR pictures following the current SDR picture using expansion functions associated to each identified third profiles to obtain a corresponding HDR picture.
Wherein having KIM’s method of tone mapping identifying a third profile in the plurality of profiles for each second determined intermediate state value, and applying an inverse tone mapping to SDR pictures following the current SDR picture using expansion functions associated to each identified third profiles to obtain a corresponding HDR picture.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD2 relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD2 there is a need to set a global tone mapping algorithm that can adapt automatically to the content to tone-map. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20170337670 A1), Paragraph [0011].
Regarding claim 20, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the method of claim 16,
KIM fails to explicitly teach wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture
However, GUERMOUD discloses wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture (Fig. 3, illustrates applying inverse tone mapping to obtain an HDR image. Paragraph [0040]-GUERMOUD discloses in a step S3, an adaptive inverse tone mapping is applied to the SDR sequence. According to the invention, the luminance range of bright video shots of the detected subsequence is mapped from the low dynamic range to a first high dynamic range while the other shots of the SDR sequence from the standard dynamic range to a second, high dynamic range, the first high dynamic range having a maximal value Lmax1 lower than the maximal value Lmax2 of the second high dynamic range (wherein the map is the expansion function and the two dynamic ranges are two profiles).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD and further in view of GUERMOUD2 of a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD of wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture
Wherein having KIM’s method of tone mapping wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD the brightness of the bright video shots having a short duration is reduced in the HDR sequence. The discomfort perceived by the viewer is thus reduced. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20180115742 A1), Paragraph [0042].
Regarding claim 21, KIM explicitly teaches a device comprising electronic circuitry configured for (Fig. 1, illustrates a device comprised of electronic circuitry. Paragraph [0044]):
obtaining a first histogram (Fig. 9, illustrates a first histogram. Paragraph [0162].) of a current standard dynamic range (SDR) picture (Fig. 8, S102 is for acquire histogram of input image. Paragraph [0147]-KIM discloses the controller 170 may acquire a histogram of an input image (S10[2]) (wherein an input image an SDR picture).), the first histogram comprising a first number of bins (Fig. 9, illustrates a histogram comprising a first number of bins. Paragraph [0147]-KIM discloses the controller 170 may acquire a histogram of an input image (S10[2]) (wherein an input image an SDR picture).), a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture (Fig. 9, illustrates bins associated with luminance levels and their associated number of occurrences. [0148-0149] KIM discloses the histogram of the input image may be a graph showing distribution of a luminance level of an input image and shows a frequency for each luminance level of the input image. For example, the controller 170 may group luminance levels of the input image in units of 32 bins (wherein luminance is a sample value).);
determining a first most representative (Fig. 9, illustrates #21 is the max bin for the first histogram. Paragraph [0165]-KIM discloses the controller 170 may acquire the max bin, and in the example of FIG. 9, the max bin may be a bin #21.) bin representative of the current SDR picture from the first histogram (Fig. 8, illustrates S102 called acquire histogram of input image (wherein the input image is an SDR picture). Paragraph [0154-0155]-KIM discloses the controller 170 may acquire the max bin and a representative bin belonging to a group with a high ratio of data (S106). The max bin may refer to a bin with the highest frequency among 32 bins. That is, the max bin may be a bin with concentrated data in the histogram of the input image (wherein the most representative bin is the max bin).), the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture (Fig. 9. Paragraph [0155]-KIM discloses the max bin may refer to a bin with the highest frequency among 32 bins. That is, the max bin may be a bin with concentrated data in the histogram of the input image (wherein the input image is an SDR picture).);
determining a first state value (Fig. 9, illustrates bins associated with luminance levels and their associated number of occurrences. [0148-0149] KIM discloses the histogram of the input image may be a graph showing distribution of a luminance level of an input image and shows a frequency for each luminance level of the input image. For example, the controller 170 may group luminance levels of the input image in units of 32 bins (wherein luminance level is a first state value).) based on the determined first representative bin (Fig. 8. Paragraph [0154-0156]-KIM discloses the controller 170 may acquire the max bin and a representative bin belonging to a group with a high ratio of data (S106). The max bin may refer to a bin with the highest frequency among 32 bins. That is, the max bin may be a bin with concentrated data in the histogram of the input image. For example, the controller 170 may acquire a bin with the highest frequency as the max bin in the histogram (wherein the luminance level of the max bin is a first state value).);
determining at least one intermediate state value (Fig. 1. Paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).) from the set of allowed transitions (Fig. 1. Paragraph [0167-0169]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio. In the example of FIG. 9, the controller 170 may determine whether a bin #21 as the max bin corresponds to any one of bins #17 to #22 as the group G6 with a high data ratio. When the max bin belongs to the group with a high data ratio, the controller 170 may acquire the max bin as an offset point. In the example of FIG. 9, the controller 170 may acquire a bin #21 as an offset point. When the max bin does not belong to the group with a high data ratio, the controller 170 may acquire a bin corresponding to an average of the max bin and the representative bin as an offset point (wherein an offset corresponding to the group with a high data ratio is an allowed transition).),
identifying a second profile (Fig. 11, illustrates a second profile (i.e. the line called OFFSET APPLIED. Paragraph [0196]-KIM discloses “offset applied” indicates an LUT curve when the offset gain is applied.) of the plurality of profiles corresponding to a first determined intermediate state value (Fig. 1. Paragraph [0160]-KIM discloses the controller 170 may obtain an offset point in order to differently apply the offset gain depending on a region in which data is concentrated. Further in paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).),
KIM fails to explicitly teach determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
However, GUERMOUD explicitly teaches determining that a transition between a previous state value and the first state value (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein L1 is a previous state value and the measured luminance that is compared to L1 is the first state value).) belongs to a set of allowed transitions (Fig. 3. Paragraph [0057]-GUERMOUD discloses the block 111 detects if the video shots are bright video shots or non-bright video shots. As mentioned above, a bright video shot is a video shot comprising at least one key frame including a bright area having a luminance value greater than L1 and a size greater than SZ1. The key frame is for example the first frame of the video shot. The block 111 delivers a signal F3 having a high level when a bright video shot is detected (wherein belonging to a set of allowed transitions is when a bright video shot is detected).), the previous state value being representative of a previous SDR picture preceding the current SDR picture (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein L1 has been determined based on testing previous images).);-
responsive to the transition is belonging to the set of allowed transitions (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein exceeding luminance and size thresholds are allowed transitions).),
responsive to the transition not belonging to the set of allowed transitions (Fig. 3. Paragraph [0039]-GUERMOUD discloses since only large areas with a high number of pixels having high luminance values will generate high values at the output of such filters, at the output of this filter any value higher than the luminance threshold L1 will be considered as indicating a bright area (wherein any value lower than L1 will not be considered and is thus not belonging to the allowed transitions).),
and applying an inverse tone mapping (Fig. 3, #12 called inverse tone mapping. Paragraph [0050]-GUERMOUD discloses a mapper 12 for applying inverse tone mapping to the SDR as a function of the results of the detector 11.) to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture (Fig. 3, illustrates applying inverse tone mapping to obtain an HDR image. Paragraph [0040]-GUERMOUD discloses in a step S3, an adaptive inverse tone mapping is applied to the SDR sequence. According to the invention, the luminance range of bright video shots of the detected subsequence is mapped from the low dynamic range to a first high dynamic range while the other shots of the SDR sequence from the standard dynamic range to a second, high dynamic range, the first high dynamic range having a maximal value Lmax1 lower than the maximal value Lmax2 of the second high dynamic range (wherein the map is the expansion function and the second high dynamic range is a second identified profile).).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM of a device comprising electronic circuitry configured for: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD of determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
Wherein having KIM’s method of tone mapping determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD the brightness of the bright video shots having a short duration is reduced in the HDR sequence. The discomfort perceived by the viewer is thus reduced. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20180115742 A1), Paragraph [0042].
KIM in view of GUERMOUD fail to explicitly teach applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
However, GUERMOUD2 explicitly teaches applying an inverse tone mapping (Fig. 3, #4 called inverse tone mapper. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.) to the current SDR picture using an expansion function (Fig. 1. Paragraph [0072]-GUERMOUD2 discloses these functions are each defined by 3 parameters: a, b and c, which means 30 parameters a, 30 parameters b and 30 parameters c for one cluster. These parameters are sorted in ascending or descending order and the median value for each parameter (a.sub.median, b.sub.median, c.sub.median) is selected as the parameters defining a function representing an expansion exponent map for a cluster. These parameters form “expansion exponent map data” of this cluster (wherein an expansion function is expansion exponent map data).) associated with a first profile of a plurality of profiles (Fig. 2. Paragraph [0073]-GUERMOUD2 discloses in the same manner, in the step S2, a visual feature is computed for each cluster based on the 30 visual features of the reference images of the cluster. This visual feature of a reference image is a feature representative of the luminance of the different reference images of the cluster. The visual feature of a reference image is for example the histogram of the luminance levels of the pixels of said reference image or image (wherein a histogram is a profile).) corresponding to the first state value (Fig. 1-2. Paragraph [0087-0088]-GUERMOUD2 discloses the visual feature of the image I is compared with the visual features of the clusters of reference images according to a distance criterion. For example, a Euclidean distance between the parameters of the visual feature of the image I and the parameters of the visual feature of each one of the clusters. In the step S5, the cluster, the visual feature of which is the closest (minimum distance) to the visual feature of the image I is selected (wherein the visual feature is the first state value).) to obtain a high dynamic range (HDR) picture (Fig. 3, illustrates the outputting of an expanded image (i.e. an HDR picture) from the inverse tone mapper #4. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.), each profile of the plurality of profiles being associated to an expansion function (Fig. 2. Paragraph [0073]-GUERMOUD2 discloses in the same manner, in the step S2, a visual feature is computed for each cluster based on the 30 visual features of the reference images of the cluster. This visual feature of a reference image is a feature representative of the luminance of the different reference images of the cluster. The visual feature of a reference image is for example the histogram of the luminance levels of the pixels of said reference image or image. Further in paragraph [0092]-GUERMOUD discloses the memory 1 stores, for each one of the clusters of reference images, expansion exponent map data and a visual feature, representative of the luminance of reference images of the cluster.); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD of a device comprising electronic circuitry configured for: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD2 of applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
Wherein having KIM’s method of tone mapping applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD2 relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD2 there is a need to set a global tone mapping algorithm that can adapt automatically to the content to tone-map. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20170337670 A1), Paragraph [0011].
Regarding claim 23, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the device of claim 21,
KIM further explicitly teaches wherein at least one second most representative bin of the first histogram is determined (Fig. 9, illustrates a histogram comprising a first number of bins. Paragraph [0147]-KIM discloses the controller 170 may acquire a histogram of an input image (S10[2]) (wherein an input image an SDR picture).), the second most representative bin being a bin of the first histogram different from the most representative bin representing one of the highest number of samples of the current SDR picture (Fig. 9, illustrates various bins with max bin, #21, and any bin in G6 representing a bin eligible to be the second most representative bin. Paragraph [0156-0157]-KIM discloses the controller 170 may acquire a bin with the highest frequency as the max bin in the histogram. The representative bin may be any one bin belonging to a group with a high ratio of data. For example, the representative bin may be the first bin of the group with a high data ratio (wherein the representative bin is the second most representative bin).), each determined most representative bin being used to determine the state value (Fig. 9. Paragraph [0148-0149]-KIM discloses the histogram of the input image may be a graph showing distribution of a luminance level of an input image and shows a frequency for each luminance level of the input image. For example, the controller 170 may group luminance levels of the input image in units of 32 bins (wherein the luminance level of a representative bin is the first state value).).
Regarding claim 24, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the device of claim 21,
KIM further explicitly teaches wherein, responsive to at least one second intermediate state value (Fig. 1. Paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).) is determined from the set of allowed transitions (Fig. 1. Paragraph [0167-0169]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio. In the example of FIG. 9, the controller 170 may determine whether a bin #21 as the max bin corresponds to any one of bins #17 to #22 as the group G6 with a high data ratio. When the max bin belongs to the group with a high data ratio, the controller 170 may acquire the max bin as an offset point. In the example of FIG. 9, the controller 170 may acquire a bin #21 as an offset point. When the max bin does not belong to the group with a high data ratio, the controller 170 may acquire a bin corresponding to an average of the max bin and the representative bin as an offset point (wherein an offset corresponding to the group with a high data ratio is an allowed transition).),
the electronic circuitry is further configured for (Fig. 1, illustrates a device comprised of electronic circuitry. Paragraph [0044]):
KIM in view of GUERMOUD fail to explicitly teach identifying a third profile in plurality of profiles for each second determined intermediate state value, and applying an inverse tone mapping to SDR pictures following the current SDR picture using expansion functions associated to the identified third profiles to obtain the HDR pictures.
However, GUERMOUD2 explicitly teaches identifying a third profile in the plurality of profiles (Fig. 2, illustrates 3 profiles (wherein the histograms are profiles and the reference images are intermediate state values). Paragraph [0064]) for each second determined intermediate state value (Fig. 2. Paragraph [0064]-GUERMOUD discloses a single expansion exponent map is computed for each cluster based on the reference expansion exponent maps of the reference images of the cluster. This step is illustrated by FIG. 2. In this figure, there are 3 clusters, each comprising 30 reference images. A function representing an expansion exponent map is generated, for each cluster, based on the 30 reference expansion exponent maps of the reference images of the cluster (wherein an intermediate state value is a reference expansion exponent map for an image).), and applying an inverse tone mapping (Fig. 3, #4 called inverse tone mapper. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.) to SDR pictures following the current SDR picture using expansion functions (Fig. 2. Paragraph [0072]-GUERMOUD2 discloses these functions are each defined by 3 parameters: a, b and c, which means 30 parameters a, 30 parameters b and 30 parameters c for one cluster. These parameters are sorted in ascending or descending order and the median value for each parameter (a.sub.median, b.sub.median, c.sub.median) is selected as the parameters defining a function representing an expansion exponent map for a cluster. These parameters form “expansion exponent map data” of this cluster (wherein an expansion function is expansion exponent map data).) associated to each identified third profiles (Fig. 2, illustrates expansion maps for the three identified profiles. Paragraph [0064]-GUERMOUD2 discloses in this figure, there are 3 clusters, each comprising 30 reference images. A function representing an expansion exponent map is generated, for each cluster, based on the 30 reference expansion exponent maps of the reference images of the cluster.) to obtain the HDR pictures (Fig. 3, illustrates the outputting of an expanded image (i.e. an HDR picture) from the inverse tone mapper #4. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD and further in view of GUERMOUD2 of a device comprising electronic circuitry configured for: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD2 of identifying a third profile in plurality of profiles for each second determined intermediate state value, and applying an inverse tone mapping to SDR pictures following the current SDR picture using expansion functions associated to the identified third profiles to obtain the HDR pictures.
Wherein having KIM’s method of tone mapping identifying a third profile in plurality of profiles for each second determined intermediate state value, and applying an inverse tone mapping to SDR pictures following the current SDR picture using expansion functions associated to the identified third profiles to obtain the HDR pictures.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD2 relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD2 there is a need to set a global tone mapping algorithm that can adapt automatically to the content to tone-map. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20170337670 A1), Paragraph [0011].
Regarding claim 25, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the device of claim 21,
KIM fails to explicitly teach wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture
However, GUERMOUD discloses wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture (Fig. 3, illustrates applying inverse tone mapping to obtain an HDR image. Paragraph [0040]-GUERMOUD discloses in a step S3, an adaptive inverse tone mapping is applied to the SDR sequence. According to the invention, the luminance range of bright video shots of the detected subsequence is mapped from the low dynamic range to a first high dynamic range while the other shots of the SDR sequence from the standard dynamic range to a second, high dynamic range, the first high dynamic range having a maximal value Lmax1 lower than the maximal value Lmax2 of the second high dynamic range (wherein the map is the expansion function and the two dynamic ranges are two profiles).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD and further in view of GUERMOUD2 of a device comprising electronic circuitry configured for: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD of wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture.
Wherein having KIM’s method of tone mapping wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD the brightness of the bright video shots having a short duration is reduced in the HDR sequence. The discomfort perceived by the viewer is thus reduced. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20180115742 A1), Paragraph [0042].
Regarding claim 31, KIM explicitly teaches a non-transitory computer readable medium (Fig. 1, #140 called storage. Paragraph [0058]) storing instructions which (Fig. 1. Paragraph [0058-0061]-KIM discloses the storage 140 can store signal-processed image, voice, or data signals stored by a program in order for each signal processing and control in the controller 170. Additionally, the storage 140 can perform a function for temporarily store image, voice, or data signals outputted from the external device interface 135 or the network interface 133 and can store information on a predetermined image through a channel memory function. The storage 140 can store an application or an application list inputted from the external device interface 135 or the network interface 133. The display device 100 can play content files (for example, video files, still image files, music files, document files, application files, and so on) stored in the storage 140 and provide them to a user.), when executed by one or more processors, cause the one or more processors to perform a method comprising (Fig. 1. Claim 1 – KIM discloses one or more processors; and a memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for:):
obtaining a first histogram (Fig. 9, illustrates a first histogram. Paragraph [0162].) of a current standard dynamic range (SDR) picture (Fig. 8, S102 is for acquire histogram of input image. Paragraph [0147]-KIM discloses the controller 170 may acquire a histogram of an input image (S10[2]) (wherein an input image an SDR picture).), the first histogram comprising a first number of bins (Fig. 9, illustrates a histogram comprising a first number of bins. Paragraph [0147]-KIM discloses the controller 170 may acquire a histogram of an input image (S10[2]) (wherein an input image an SDR picture).), a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture (Fig. 9, illustrates bins associated with luminance levels and their associated number of occurrences. [0148-0149] KIM discloses the histogram of the input image may be a graph showing distribution of a luminance level of an input image and shows a frequency for each luminance level of the input image. For example, the controller 170 may group luminance levels of the input image in units of 32 bins (wherein luminance is a sample value).);
determining a first most representative bin (Fig. 9, illustrates #21 is the max bin for the first histogram. Paragraph [0165]-KIM discloses the controller 170 may acquire the max bin, and in the example of FIG. 9, the max bin may be a bin #21.) representative of the current SDR picture from the first histogram (Fig. 8, illustrates S102 called acquire histogram of input image (wherein the input image is an SDR picture). Paragraph [0154-0155]-KIM discloses the controller 170 may acquire the max bin and a representative bin belonging to a group with a high ratio of data (S106). The max bin may refer to a bin with the highest frequency among 32 bins. That is, the max bin may be a bin with concentrated data in the histogram of the input image (wherein the most representative bin is the max bin).), the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture (Fig. 9. Paragraph [0155]-KIM discloses the max bin may refer to a bin with the highest frequency among 32 bins. That is, the max bin may be a bin with concentrated data in the histogram of the input image (wherein the input image is an SDR picture).);
determining a first state value (Fig. 9, illustrates bins associated with luminance levels and their associated number of occurrences. [0148-0149] KIM discloses the histogram of the input image may be a graph showing distribution of a luminance level of an input image and shows a frequency for each luminance level of the input image. For example, the controller 170 may group luminance levels of the input image in units of 32 bins (wherein luminance level is a first state value).) based on the determined first representative bin (Fig. 8. Paragraph [0154-0156]-KIM discloses the controller 170 may acquire the max bin and a representative bin belonging to a group with a high ratio of data (S106). The max bin may refer to a bin with the highest frequency among 32 bins. That is, the max bin may be a bin with concentrated data in the histogram of the input image. For example, the controller 170 may acquire a bin with the highest frequency as the max bin in the histogram (wherein the luminance level of the max bin is a first state value).);
determining at least one intermediate state value (Fig. 1. Paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).) from the set of allowed transitions (Fig. 1. Paragraph [0167-0169]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio. In the example of FIG. 9, the controller 170 may determine whether a bin #21 as the max bin corresponds to any one of bins #17 to #22 as the group G6 with a high data ratio. When the max bin belongs to the group with a high data ratio, the controller 170 may acquire the max bin as an offset point. In the example of FIG. 9, the controller 170 may acquire a bin #21 as an offset point. When the max bin does not belong to the group with a high data ratio, the controller 170 may acquire a bin corresponding to an average of the max bin and the representative bin as an offset point (wherein an offset corresponding to the group with a high data ratio is an allowed transition).),
identifying a second profile of the plurality of profiles (Fig. 11, illustrates a second profile (i.e. the line called OFFSET APPLIED. Paragraph [0196]-KIM discloses “offset applied” indicates an LUT curve when the offset gain is applied.) corresponding to a first determined intermediate state value (Fig. 1. Paragraph [0160]-KIM discloses the controller 170 may obtain an offset point in order to differently apply the offset gain depending on a region in which data is concentrated. Further in paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).), and
KIM fails to explicitly teach determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
However, GUERMOUD explicitly teaches determining that a transition between a previous state value and the first state value (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein L1 is a previous state value and the measured luminance that is compared to L1 is the first state value).) belongs to a set of allowed transitions (Fig. 3. Paragraph [0057]-GUERMOUD discloses the block 111 detects if the video shots are bright video shots or non-bright video shots. As mentioned above, a bright video shot is a video shot comprising at least one key frame including a bright area having a luminance value greater than L1 and a size greater than SZ1. The key frame is for example the first frame of the video shot. The block 111 delivers a signal F3 having a high level when a bright video shot is detected (wherein belonging to a set of allowed transitions is when a bright video shot is detected).), the previous state value being representative of a previous SDR picture preceding the current SDR picture (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein L1 has been determined based on testing previous images).);-
responsive to the transition is belonging to the set of allowed transitions (Fig. 3. Paragraph [0039]-GUERMOUD discloses a bright video shot designates a video shot comprising at least one key frame including a bright area having a luminance value greater than a luminance threshold L1 and a size (i.e. surface) greater than a size threshold SZ1. The luminance threshold L1 is for example equal to ⅔ of the maximum value of SDR luminance range and the size threshold SZ1 is for example equal to 1/16 of the total size of the image (wherein exceeding luminance and size thresholds are allowed transitions).),
responsive to the transition not belonging to the set of allowed transitions (Fig. 3. Paragraph [0039]-GUERMOUD discloses since only large areas with a high number of pixels having high luminance values will generate high values at the output of such filters, at the output of this filter any value higher than the luminance threshold L1 will be considered as indicating a bright area (wherein any value lower than L1 will not be considered and is thus not belonging to the allowed transitions).),
applying an inverse tone mapping (Fig. 3, #12 called inverse tone mapping. Paragraph [0050]-GUERMOUD discloses a mapper 12 for applying inverse tone mapping to the SDR as a function of the results of the detector 11.) to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture (Fig. 3, illustrates applying inverse tone mapping to obtain an HDR image. Paragraph [0040]-GUERMOUD discloses in a step S3, an adaptive inverse tone mapping is applied to the SDR sequence. According to the invention, the luminance range of bright video shots of the detected subsequence is mapped from the low dynamic range to a first high dynamic range while the other shots of the SDR sequence from the standard dynamic range to a second, high dynamic range, the first high dynamic range having a maximal value Lmax1 lower than the maximal value Lmax2 of the second high dynamic range (wherein the map is the expansion function and the second high dynamic range is a second identified profile).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM of a non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD of determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
Wherein having KIM’s method of tone mapping determining that a transition between a previous state value and the first state value belongs to a set of allowed transitions, the previous state value being representative of a previous SDR picture preceding the current SDR picture; responsive to the transition is belonging to the set of allowed transitions, and responsive to the transition not belonging to the set of allowed transitions and applying an inverse tone mapping to the current SDR picture using an expansion function associated to the identified second profile to obtain the HDR picture.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD the brightness of the bright video shots having a short duration is reduced in the HDR sequence. The discomfort perceived by the viewer is thus reduced. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20180115742 A1), Paragraph [0042].
KIM in view of GUERMOUD fail to explicitly teach applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
However, GUERMOUD2 explicitly teaches applying an inverse tone mapping (Fig. 3, #4 called inverse tone mapper. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.) to the current SDR picture using an expansion function (Fig. 1. Paragraph [0072]-GUERMOUD2 discloses these functions are each defined by 3 parameters: a, b and c, which means 30 parameters a, 30 parameters b and 30 parameters c for one cluster. These parameters are sorted in ascending or descending order and the median value for each parameter (a.sub.median, b.sub.median, c.sub.median) is selected as the parameters defining a function representing an expansion exponent map for a cluster. These parameters form “expansion exponent map data” of this cluster (wherein an expansion function is expansion exponent map data).) associated with a first profile of a plurality of profiles (Fig. 2. Paragraph [0073]-GUERMOUD2 discloses in the same manner, in the step S2, a visual feature is computed for each cluster based on the 30 visual features of the reference images of the cluster. This visual feature of a reference image is a feature representative of the luminance of the different reference images of the cluster. The visual feature of a reference image is for example the histogram of the luminance levels of the pixels of said reference image or image (wherein a histogram is a profile).) corresponding to the first state value (Fig. 1-2. Paragraph [0064]-GUERMOUD2 discloses a single expansion exponent map is computed for each cluster based on the reference expansion exponent maps of the reference images of the cluster. This step is illustrated by FIG. 2. In this figure, there are 3 clusters, each comprising 30 reference images. A function representing an expansion exponent map is generated, for each cluster, based on the 30 reference expansion exponent maps of the reference images of the cluster. Paragraph [0087-0088]-GUERMOUD2 discloses the visual feature of the image I is compared with the visual features of the clusters of reference images according to a distance criterion. For example, a Euclidean distance between the parameters of the visual feature of the image I and the parameters of the visual feature of each one of the clusters. In the step S5, the cluster, the visual feature of which is the closest (minimum distance) to the visual feature of the image I is selected (wherein a first state value is an expansion exponent map for a cluster, as shown in fig. 2).) to obtain a high dynamic range (HDR) picture (Fig. 3, illustrates the outputting of an expanded image (i.e. an HDR picture) from the inverse tone mapper #4. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.),
each profile of the plurality of profiles being associated to an expansion function (Fig. 2. Paragraph [0073]-GUERMOUD2 discloses in the same manner, in the step S2, a visual feature is computed for each cluster based on the 30 visual features of the reference images of the cluster. This visual feature of a reference image is a feature representative of the luminance of the different reference images of the cluster. The visual feature of a reference image is for example the histogram of the luminance levels of the pixels of said reference image or image. Further in paragraph [0092]-GUERMOUD discloses the memory 1 stores, for each one of the clusters of reference images, expansion exponent map data and a visual feature, representative of the luminance of reference images of the cluster.); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD of a non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD2 of applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
Wherein having KIM’s method of tone mapping applying an inverse tone mapping to the current SDR picture using an expansion function associated with a first profile of a plurality of profiles corresponding to the first state value to obtain a high dynamic range (HDR) picture, each profile of the plurality of profiles being associated to an expansion function.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD2 relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD2 there is a need to set a global tone mapping algorithm that can adapt automatically to the content to tone-map. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20170337670 A1), Paragraph [0011].
Regarding claim 33, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the non-transitory computer readable medium of claim 31,
KIM further explicitly teaches wherein at least one second most representative bin is determined from the first histogram (Fig. 9, illustrates a histogram comprising a first number of bins. Paragraph [0147]-KIM discloses the controller 170 may acquire a histogram of an input image (S10[2]) (wherein an input image an SDR picture).), a second most representative bin being a bin of the first histogram different from the most representative bin representing one of the highest number of samples of the current SDR picture (Fig. 9, illustrates various bins with max bin, #21, and any bin in G6 representing a bin eligible to be the second most representative bin. Paragraph [0156-0157]-KIM discloses the controller 170 may acquire a bin with the highest frequency as the max bin in the histogram. The representative bin may be any one bin belonging to a group with a high ratio of data. For example, the representative bin may be the first bin of the group with a high data ratio (wherein the representative bin is the second most representative bin).), each determined most representative bin being used to identify determine the first state value (Fig. 9. Paragraph [0148-0149]-KIM discloses the histogram of the input image may be a graph showing distribution of a luminance level of an input image and shows a frequency for each luminance level of the input image. For example, the controller 170 may group luminance levels of the input image in units of 32 bins (wherein the luminance level of a representative bin is the first state value).).
Regarding claim 34, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the non-transitory computer readable medium of claim 31,
KIM further explicitly teaches wherein the instructions further cause the one or more processors to perform, comprising, responsive to at least one second intermediate state value (Fig. 1. Paragraph [0167]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio (wherein an offset point is an intermediate state value).) is determined from the set of allowed transitions (Fig. 1. Paragraph [0167-0169]-KIM discloses to acquire the offset point, the controller 170 may determine whether the max bin belongs to the group with a high data ratio. In the example of FIG. 9, the controller 170 may determine whether a bin #21 as the max bin corresponds to any one of bins #17 to #22 as the group G6 with a high data ratio. When the max bin belongs to the group with a high data ratio, the controller 170 may acquire the max bin as an offset point. In the example of FIG. 9, the controller 170 may acquire a bin #21 as an offset point. When the max bin does not belong to the group with a high data ratio, the controller 170 may acquire a bin corresponding to an average of the max bin and the representative bin as an offset point (wherein an offset corresponding to the group with a high data ratio is an allowed transition).),
KIM in view of GUERMOUD fail to explicitly teach identifying a third profile in the plurality of profiles for each second determined intermediate state value, and applying an inverse tone mapping to SDR pictures following the current SDR picture using expansion functions associated to each identified third profiles to obtain a corresponding HDR picture.
However, GUERMOUD2 explicitly teaches identifying a third profile in the plurality of profiles (Fig. 2, illustrates 3 profiles (wherein the histograms are profiles and the reference images are intermediate state values). Paragraph [0064]) for each second determined intermediate state value (Fig. 2. Paragraph [0064]-GUERMOUD discloses a single expansion exponent map is computed for each cluster based on the reference expansion exponent maps of the reference images of the cluster. This step is illustrated by FIG. 2. In this figure, there are 3 clusters, each comprising 30 reference images. A function representing an expansion exponent map is generated, for each cluster, based on the 30 reference expansion exponent maps of the reference images of the cluster (wherein an intermediate state value is a reference expansion exponent map for an image).), and applying an inverse tone mapping (Fig. 3, #4 called inverse tone mapper. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.) to SDR pictures following the current SDR picture using expansion functions (Fig. 2. Paragraph [0072]-GUERMOUD2 discloses these functions are each defined by 3 parameters: a, b and c, which means 30 parameters a, 30 parameters b and 30 parameters c for one cluster. These parameters are sorted in ascending or descending order and the median value for each parameter (a.sub.median, b.sub.median, c.sub.median) is selected as the parameters defining a function representing an expansion exponent map for a cluster. These parameters form “expansion exponent map data” of this cluster (wherein an expansion function is expansion exponent map data).) associated to each identified third profiles (Fig. 2, illustrates expansion maps for the three identified profiles. Paragraph [0064]-GUERMOUD2 discloses in this figure, there are 3 clusters, each comprising 30 reference images. A function representing an expansion exponent map is generated, for each cluster, based on the 30 reference expansion exponent maps of the reference images of the cluster.) to obtain a corresponding HDR picture (Fig. 3, illustrates the outputting of an expanded image (i.e. an HDR picture) from the inverse tone mapper #4. Paragraph [0095]-GUERMOUD2 discloses the inverse tone mapper 4 applies the expansion exponent map computed using a function defined by expansion exponent map data of the selected cluster to the image I as defined in step S6, i.e. by parameters a, b and c defining a quadratic function.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD and further in view of GUERMOUD2 of a non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD2 of identifying a third profile in the plurality of profiles for each second determined intermediate state value, and applying an inverse tone mapping to SDR pictures following the current SDR picture using expansion functions associated to each identified third profiles to obtain a corresponding HDR picture.
Wherein having KIM’s method of tone mapping identifying a third profile in the plurality of profiles for each second determined intermediate state value, and applying an inverse tone mapping to SDR pictures following the current SDR picture using expansion functions associated to each identified third profiles to obtain a corresponding HDR picture.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD2 relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD2 there is a need to set a global tone mapping algorithm that can adapt automatically to the content to tone-map. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20170337670 A1), Paragraph [0011].
Regarding claim 35, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the non-transitory computer readable medium of claim 31,
KIM fails to explicitly teach wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture
However, GUERMOUD discloses wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture (Fig. 3, illustrates applying inverse tone mapping to obtain an HDR image. Paragraph [0040]-GUERMOUD discloses in a step S3, an adaptive inverse tone mapping is applied to the SDR sequence. According to the invention, the luminance range of bright video shots of the detected subsequence is mapped from the low dynamic range to a first high dynamic range while the other shots of the SDR sequence from the standard dynamic range to a second, high dynamic range, the first high dynamic range having a maximal value Lmax1 lower than the maximal value Lmax2 of the second high dynamic range (wherein the map is the expansion function and the two dynamic ranges are two profiles).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD and further in view of GUERMOUD2 of a non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of GUERMOUD of wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture
Wherein having KIM’s method of tone mapping wherein the expansion function used for the inverse tone mapping of the current SDR picture is based on the expansion function associated to the first or second profile and on at least one expansion function determined for a SDR picture preceding the current SDR picture
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and GUERMOUD relate to tone mapping and converting to HDR, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while GUERMOUD the brightness of the bright video shots having a short duration is reduced in the HDR sequence. The discomfort perceived by the viewer is thus reduced. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and GUERMOUD et al. (US 20180115742 A1), Paragraph [0042].
Claims 17, 22, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over KIM et al. (US 20220277428 A1), hereinafter referenced as KIM, in view of GUERMOUD et al. (US 20180115742 A1), hereinafter referenced as GUERMOUD, and further in view of GUERMOUD et al. (US A1), hereinafter referenced as GUERMOUD2, and further in view of WOODALL (US 20230117976 A1), hereinafter referenced as WOODALL.
Regarding claim 17, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the method of claim 16,
KIM in view of GUERMOUD and further in view of GUERMOUD2 fail to explicitly teach
However, WOODALL explicitly teaches wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins (Fig. 7A-7I, illustrate various histograms being downsampled to have fewer number of bins than the initial histograms. Paragraph [0039]-WOODALL discloses histograms of the desired resolution can be generated by downsampling the high resolution histogram. The result for the three different input distributions of FIG. 7A after bin averaging, upsampling, filtering, and downsampling are shown in FIGS. 7E, 7G, and 7I.), the first number of bins being less than the second number of bins (Fig. 7A-7I, illustrate various histograms being downsampled to have fewer number of bins than the initial histograms. Paragraph [0069]-WOODALL discloses in FIG. 7E, the first filtered upsampled histogram 742 is downsampled to form a first downsampled histogram 752. Downsampling is accomplished by adding the pixel intensity values of neighboring bins together. For example, if the downsampling is done at a ratio of 2:1, the contents of each two neighboring bins are added to form one new bin. Similarly, in FIG. 7G, a second downsampled histogram 772 is the result of downsampling the second filtered upsampled histogram 762 and FIG. 7I a third downsampled histogram 792 represents a downsampling of the third filtered upsampled histogram 782.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD and further in view of GUERMOUD2 of a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of WOODALL of wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins, the first number of bins being less than the second number of bins.
Wherein having KIM’s method of tone mapping wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins, the first number of bins being less than the second number of bins.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and WOODALL relate to tone mapping and analyzing histograms of an image, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while WOODALL various picture quality improvement algorithms may be efficiently combined during image processing in a way that reduces visual artifacts without increasing the time for those effects to be applied. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and WOODALL (US 20230117976 A1), Paragraph [0004].
Regarding claim 22, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the device of claim 21,
KIM in view of GUERMOUD and further in view of GUERMOUD2 fail to explicitly teach wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins, the first number of bins being less than the second number of bins.
However, WOODALL explicitly teaches wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins (Fig. 7A-7I, illustrate various histograms being downsampled to have fewer number of bins than the initial histograms. Paragraph [0039]-WOODALL discloses histograms of the desired resolution can be generated by downsampling the high resolution histogram. The result for the three different input distributions of FIG. 7A after bin averaging, upsampling, filtering, and downsampling are shown in FIGS. 7E, 7G, and 7I.), the first number of bins being less than the second number of bins (Fig. 7A-7I, illustrate various histograms being downsampled to have fewer number of bins than the initial histograms. Paragraph [0069]-WOODALL discloses in FIG. 7E, the first filtered upsampled histogram 742 is downsampled to form a first downsampled histogram 752. Downsampling is accomplished by adding the pixel intensity values of neighboring bins together. For example, if the downsampling is done at a ratio of 2:1, the contents of each two neighboring bins are added to form one new bin. Similarly, in FIG. 7G, a second downsampled histogram 772 is the result of downsampling the second filtered upsampled histogram 762 and FIG. 7I a third downsampled histogram 792 represents a downsampling of the third filtered upsampled histogram 782.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD and further in view of GUERMOUD2 of a device comprising electronic circuitry configured for: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of WOODALL of wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins, the first number of bins being less than the second number of bins.
Wherein having KIM’s method of tone mapping wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins, the first number of bins being less than the second number of bins.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and WOODALL relate to tone mapping and analyzing histograms of an image, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while WOODALL various picture quality improvement algorithms may be efficiently combined during image processing in a way that reduces visual artifacts without increasing the time for those effects to be applied. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and WOODALL (US 20230117976 A1), Paragraph [0004].
Regarding claim 32, KIM in view of GUERMOUD and further in view of GUERMOUD2 explicitly teach the non-transitory computer readable medium of claim 31,
KIM in view of GUERMOUD and further in view of GUERMOUD2 fail to explicitly teach wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins, the first number of bins being less than the second number of bins.
However, WOODALL explicitly teaches wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins (Fig. 7A-7I, illustrate various histograms being downsampled to have fewer number of bins than the initial histograms. Paragraph [0039]-WOODALL discloses histograms of the desired resolution can be generated by downsampling the high resolution histogram. The result for the three different input distributions of FIG. 7A after bin averaging, upsampling, filtering, and downsampling are shown in FIGS. 7E, 7G, and 7I.), the first number of bins being less than the second number of bins (Fig. 7A-7I, illustrate various histograms being downsampled to have fewer number of bins than the initial histograms. Paragraph [0069]-WOODALL discloses in FIG. 7E, the first filtered upsampled histogram 742 is downsampled to form a first downsampled histogram 752. Downsampling is accomplished by adding the pixel intensity values of neighboring bins together. For example, if the downsampling is done at a ratio of 2:1, the contents of each two neighboring bins are added to form one new bin. Similarly, in FIG. 7G, a second downsampled histogram 772 is the result of downsampling the second filtered upsampled histogram 762 and FIG. 7I a third downsampled histogram 792 represents a downsampling of the third filtered upsampled histogram 782.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KIM in view of GUERMOUD and further in view of GUERMOUD2 of a non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform a method comprising: obtaining a first histogram of a current standard dynamic range (SDR) picture, the first histogram comprising a first number of bins, a bin associating a sample value to a number of occurrences of the sample value in the current SDR picture; determining a first most representative bin representative of the current SDR picture from the first histogram, the first most representative bin being the bin of the first histogram representing a highest number of samples of the current SDR picture with the teachings of WOODALL of wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins, the first number of bins being less than the second number of bins.
Wherein having KIM’s method of tone mapping wherein the first histogram is obtained from a second histogram of the current SDR picture comprising a second number of bins, the first number of bins being less than the second number of bins.
The motivation behind the modification would have been to obtain a method of tone mapping that improves the coloring and contrast of an image in order to improve a user’s view of an image. Since both KIM and WOODALL relate to tone mapping and analyzing histograms of an image, wherein KIM there is a need for a method of improving color or contrast irrespective of the characteristics of an image, while WOODALL various picture quality improvement algorithms may be efficiently combined during image processing in a way that reduces visual artifacts without increasing the time for those effects to be applied. Please see KIM et al. (US 20220277428 A1), Paragraph [0006], and WOODALL (US 20230117976 A1), Paragraph [0004].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN N WOLFSON whose telephone number is (571)272-1898. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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/ETHAN N WOLFSON/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
