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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of application EP23207885.7 has been received.
Response to Amendment
The amendment filed July 31st, 2026 has been entered. Claims 1, 5-12, and 14-17 are pending in the application. The applicant’s amendments to the claims have overcome the antecedent basis rejection of claim 6 under 35 USC § 112 previously set forth in the Non-Final Office Action of May 5th, 2026.
Response to Arguments
Applicant's arguments filed July 31st, 2026 have been fully considered but they are not persuasive.
Applicant states (Remarks p. 1-2):
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The attorney argues the amendment provides sufficient definition to clarify the meaning of the claim terms "minimum target cone response," "delta cone response," and "adjusted cone response." The examiner disagrees because listing generally what terms are calculated from does nor provide sufficient structure or clarity for the aforementioned terms and the terms remain indefinite. The amendments in question include:
“calculating a minimum target cone response from the local adaptation pooling and the target retinal image”
“calculating a delta cone response under the ideal surround conditions”
“calculating an adjusted target cone response by combining the minimum target cone response and the delta cone response;”
The term “minimum target cone response” is still indefinite at least because the meaning of the term is unclear in the context of local adaptation pooling and the target retinal image. The term “delta cone response” is still indefinite at least because the amendment does not provide additional context that would sufficiently clarify to a person of ordinary skill in the art the meaning of the term “delta cone response”. The term “adjusted target cone response” is indefinite at least because the meaning of the term is unclear in the context of the “minimum target cone response” and the “delta cone response”.
Applicant states (Remarks p. 2):
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The examiner disagrees because the paragraph 0074 of specification merely describes when targeting an “absolute cone response” is effective and what conditions may yield an “absolute cone response”; however, the meaning of the term “absolute cone response” is not defined nor made sufficiently clear by the specification. The term “absolute cone response” is indefinite at least because neither the amended claims nor the specification provide sufficiently clarity to a person of ordinary skill in the art as to the meaning of the term delta cone response.
Applicant states (Remarks p. 3):
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The examiner disagrees because applicant urges patentability based on a specific computational order recited in claims 1 and 12; however, the claims do not recite a specific computational order. For example, at least the limitations “determining ideal surround conditions based on the target device;” and / or “calculating a delta cone response under the ideal surround conditions;” can occur at any point in between, before, or after the limitations that precede them.
Further the examiner acknowledges that some of the limitations have order with respect to some of the other limitations. In those cases, that computational order is respected in the claim mapping in the body of the rejection below.
Applicant states (Remarks p. 3-4):
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The examiner disagrees because “In order for a reference to be proper for use in an obviousness rejection under 35 U.S.C. 103 , the reference must be analogous art to the claimed invention. In re Bigio, 381 F.3d 1320, 1325, 72 USPQ2d 1209, 1212 (Fed. Cir. 2004). A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention),” (MPEP 2141.01(a), subsection I). Aydin is considered analogous art to the claimed invention at least because both are in the field of image processing.
Regarding applicants’ assertion that “Aydin does not determine a target retinal image, calculate a minimum target cone response, determine a delta cone response under ideal surround conditions, combine those cone responses to produce an adjusted cone response, or reconstruct target luminance from those cone responses,” Aydin is not relied upon for teaching those portions of the claims.
Furthermore, 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 computational reconstruction of image luminance based upon modeled retinal responses under target and ideal viewing conditions") 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).
Applicant states (Remarks p. 4):
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In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Further, Huang teaches calculating a delta cone response under the ideal surround conditions, calculating an adjusted target cone response by combining the minimum target cone response and the delta cone response, the target luminance is calculated from the enhanced background luminance layer and the adjusted target cone response, and determining a delta cone response based on ideal surround conditions.
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Huang to Aydin in view of Greenebaum in further view of Vangorp. The motivation would have been to "…[boost] luminance of image areas below a perceptual threshold while preserving contrast of other image areas," (Huang; page 1, para [0008]) and / or to improve picture quality and perceived picture quality.
Applicant states (Remarks p. 4-5):
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The examiner disagrees because Vangorp teaches calculating a minimum target cone response. Under the broadest reasonable interpretation a “minimum target cone response” includes Vangorp’s detection threshold. This is particularly true in in view of the indefiniteness of the claims. Further, Vangorp is not relied upon teaching determine a delta cone response under ideal surround conditions, combine those responses to obtain an adjusted cone response, or reconstruct target luminance from the adjusted cone response.
Further, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant states (Remarks p. 5):
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The remarks urge patentability of the independent claims. Applicants’ arguments with respect to the independent claims regarding Talvala have been considered but are moot because the new ground of rejection for the independent claims does not rely on Talvala applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new ground(s) of rejection is necessitated by the amendments to the independent claims.
Applicant states (Remarks p. 5):
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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, before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Greenebaum to Aydin. The motivation would have been to enable dynamic adjustment of the display such that a viewer's perception of the displayed data remains relatively stable despite changes to the ambient conditions in which the display device is being viewed and / or to visually please the user.
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Vangorp to Aydin in view of Greenebaum. The motivation would have been to “[derive] error bounds for physically based rendering, [determine] the backlight resolution for HDR displays, [measure] the maximum visible dynamic range in complex natural scenes, [simulate] afterimages, and gaze-dependent tone mapping,” (Vangorp; page 1, abstract) and / or to improve picture quality.
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Huang to Aydin in view of Greenebaum in further view of Vangorp. The motivation would have been to "…[boost] luminance of image areas below a perceptual threshold while preserving contrast of other image areas," (Huang; page 1, para [0008]) and / or to improve picture quality and perceived picture quality.
Further, in response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 5-11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the limitation “determining ideal surround conditions based on the target device;” is not supported by the specification (specification, para [0031]-[0035]), as such the claims are drawn to new matter. The specification merely discloses an example of what “ideal surround conditions” could be but not if or how the “ideal surround conditions” are determined. The specification also indicates that “ideal surround conditions can change depending on application,” but does not disclose a determining step (specification, para [0033]).
Claims 5-11 are rejected due to dependency on claim 1.
Claims 12 and 14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 12, the limitation “calculating local adaptation pooling from a local adaptation kernel and the ideal retinal image;” is not supported by the specification, as such the claims are drawn to new matter. There is no mention of the term “ideal retinal image” in the specification nor any indication of what an “ideal retinal image” may be.
Claim 14 is rejected due to dependency on claim 12.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 5-12, and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the term “minimum target cone response” is unclear and the claim is indefinite. The term “minimum” in “minimum target cone response” is a relative term which renders the claim indefinite. The term “minimum target cone response” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Additionally, there is no clear explanation of what a “target cone response” is. The specification discloses “calculate the target cone response as the sum of the target black response plus the delta cone response,” (specification para [0093]). The specification defines a target cone response relative to a “target black response”; however; the “target black response” has no associated definition and is not a standard term in the art. There is no sufficient explanation for what a “minimum target cone response” is. The specification does not provide guidance on how to determine the metes and bounds of the term “minimum target cone response.”
Further limiting the claim to include: “calculating a minimum target cone response from the local adaptation pooling and the target retinal image” does not provide sufficient limitation or clarity to the term for one of ordinary skill in the art to determine the metes and bounds of the claim.
Further limiting the claim to include: “calculating an adjusted target cone response by combining the minimum target cone response and the delta cone response;” does not provide sufficient limitation or clarity to the term for one of ordinary skill in the art to determine the metes and bounds of the claim.
For the purpose of compact prosecution and art rejection, the examiner will interpret the term “minimum target cone response” to mean the minimum threshold for human cone vision/ detection.
Further regarding claim 1, the term “delta cone response” is unclear and indefinite. The term “delta cone response” is not a standard term in the art. The specification does not provide guidance on how to determine the metes and bounds of the term “delta cone response,” (specification, para [0042]-[0050]).
Further limiting the claim to include: “calculating a delta cone response under the ideal surround conditions” does not provide sufficient limitation or clarity to the term for one of ordinary skill in the art to determine the metes and bounds of the claim.
For the purpose of compact prosecution and art rejection, the examiner will interpret the term “delta cone response” to represent a change or difference in human cone response.
Regarding 12, it is rejected using the same citations and rationales described in the rejection of claim 1.
Claims 5-11 are rejected due to dependency on claim 1.
Claim 14 is rejected due to dependency on claim 12.
Further regarding claim 12, it recites the limitation "the ideal retinal image" in lines 6-7 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Further regarding claim 14, the term “absolute cone response value” is unclear and indefinite. The explanation in the specification (specification, para [0074]-[0075]) has no clear correlation to the plain meaning of the term. The specification provides no definition for “absolute cone response value” nor guidance on how to determine the metes and bounds of the term “absolute cone response value.” For the purpose of compact prosecution and art rejection, the examiner will interpret the term “adjusted cone response value” to mean the adjusted cone response is calculated and a result is used as a cone response value.
Claim Rejections - 35 USC § 103
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 1, 5, 7-9, and 11-12, 14- 17 are rejected under 35 U.S.C. 103 as being unpatentable over Aydin et al. (US 20180218709 A1; hereinafter Aydin) in view of Greenebaum et al. (US 20170092229 A1; hereinafter Greenebaum) in further view of Vangorp et al. "A Model of Local Adaptation," November 2015, ACM Transactions on Graphics, Vol. 34, pages 1-13; (hereinafter Vangorp) in further view of Huang (US 20150029205 A1; hereinafter Huang).
Regarding claim 1, Aydin teaches a method to modify an image displayed for a user on a target device in target surround conditions ("...However, content that is filmed in HDR and/or presented on an HDR display may have downsides associated with the extended dynamic range. For example, a viewer's visual system may become strained during abrupt transitions from dark frames of content to much brighter frames of content. This can lead to viewing discomfort.... Further still, the computer-implemented method comprises adjusting luminance of the media content to comport with one or more desired luminance-based effects. In one aspect, the analyzing of the media content comprises determining a luminance level associated with a pixel of a frame of the media content. In another aspect, the analyzing of the media content comprises determining a luminance level associated with a spatial neighborhood approximately about the pixel. In still another aspect, the analyzing of the media content comprises determining an ambient luminance level relative to the pixel," (page 1, para [0002] - [0003]; page 2, para [0020]).
“It should be noted that ambient luminance can refer to lighting other than that emanating from a display or screen on which content is presented. This can include, for example, ceiling lights, lamps, or other light sources in a room where a display is located,” (page 3, para [0030]).
Adjusting luminance of the media content reads on modify an image. A viewer's visual system reads on displayed for a user. An HDR display is a component of a device. The target surround conditions include the ambient luminance.), said method comprising:
calculating a target luminance from the local adaptation pooling "In some embodiments, the computer-implemented method further comprises applying a pooling function to combine the one or more corresponding levels of perceived luminance discomfort associated with determined luminance levels of one or more pixels of a frame of the media content, the combination of the one or more corresponding levels of perceived luminance discomfort comprising a frame-wide estimate of perceived luminance discomfort. Each of the one or more corresponding levels of perceived luminance discomfort comprises a subjective determination of discomfort experienced during exposure to test media content having commensurate luminance characteristics as the analyzed media content," (page 1, para [0005])
"Accordingly, some embodiments of the present disclosure may implement a “pooling function” to avoid analyzing content in a manner that is overly granular. For example, a frame of video content may contain a subset of pixels representative of a relatively small spotlight that does not impact a viewer's perception of the overall luminance of that frame. A pooling function can be utilized to adapt the maladaptation model for use with some larger subset of pixels to get a more accurate representation of luminance in the frame," (page 3, para [0035]).
“It should be understood that the above transducer function incorporates a mapping function from {circumflex over (L)}.sup.t to {circumflex over (D)}.sup.t that minimizes ∥D.sup.t−D.sup.t∥.sup.2 over all the obtained subjective data to achieve data that is normalized/improve data integrity. That is, transducer function τ is designed to minimize the difference of test data D.sup.t to predicted luminance discomfort D.sup.t. Additionally, for practical reasons, as noted above, transfer function τ can be defined per-frame (or some other subset) rather than per-pixel. Thus, the aforementioned pooling function may combine per-pixel luminance discomfort estimates or predictions into frame-wide luminance discomfort estimates or predictions,” (page 4, para [0044]).
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(page 3, para [0029]).
"In some embodiments, the system may further comprise a post-processing system having computer code being executed to cause the post-processing system to adjust luminance of the media content based upon the one or more estimates of perceived luminance discomfort," (page 1, para [0010]; page 4, para [0045] - [0047]).
“…a director may utilize post-processing system 208 to apply a mathematical optimization function to adjust the mean luminance of an entire movie to ensure that a perceived luminance discomfort level of 3 is never exceeded,” (page 4, para [0045] - [0047]).
The adjusted mean luminance (Lt) reads on the target luminance. The pooling function is used to determine the perceived luminance discomfort which in turn is used to calculate the luminance adjustment. The pooling function represents a local adaptation pooling because the pooling function includes per-pixel predictions/ adaptations and spatial grouping comprising the subset of pixels representative of a relatively small spotlight. The disclosed pooling function is consistent with the definition given in the specification (specification, para [0021]).);
modifying the image by the target luminance to produce an adapted image ("In some embodiments, the system may further comprise a post-processing system having computer code being executed to cause the post-processing system to adjust luminance of the media content based upon the one or more estimates of perceived luminance discomfort," (page 1, para [0010]; page 4, para [0045] - [0047]).
“some embodiments may further rely on the perceived luminance discomfort to adjust the mean luminance, Lt, of each video frame,” (page 4, para [0046]).
“…a director may utilize post-processing system 208 to apply a mathematical optimization function to adjust the mean luminance of an entire movie to ensure that a perceived luminance discomfort level of 3 is never exceeded,” (page 4, para [0045] - [0047]).
Adjusting the luminance of the media content reads on modifying the image. Aydin is adjusting the luminance of the media content by the adjusted mean luminance (Lt) (reads on target luminance). The media content after the luminance adjustment is the adapted image.).
Aydin is not relied upon teaching but Greenebaum teaches modeling a target surround image corresponding to the target surround conditions (Greenebaum; “In one embodiment, the optical sensor 404 may comprise a video camera capable of capturing spatial information, color information, and intensity information. Thus, utilizing a video camera could allow for the creation of an ambient model that could adapt not only the gamma and black point of the display device, but also the display device's white point,” (page 6, para [0050]).
“In one embodiment, the black level for a given ambient environment may be determined, e.g., by using an ambient light sensor 404 or by taking measurements from the display device's actual panel and/or diffuser. As mentioned above in reference to FIG. 4, diffuse reflection of ambient light off the surface of the device may cause a certain range of the darkest display levels to become indiscernible to the viewer. Generally, ambient light as reflected off the display, as well as backlight that is not stopped by the display at the blackest values combine additively to create a so-called “pedestal.” Pedestals does not technically mask display values but rather make all displayed values brighter by the “pedestal” amount. Stated more directly, ambient light as viewed reflected off surfaces changes the user's adaptation… Once this level of diffuse reflection is determined, the black point may be adjusted accordingly. For example, if all luminance values below an 8-bit value of 40 would be indiscernible to the viewer over the level of diffuse reflection (though this is likely an extreme example), the system 700 may set the black point to be 40, thus compressing the pixel luminance values into the range of 41-255.,” (pages 8-9, para [0065]).
Optical sensor 404 comprises ambient light sensor 404. The creation of the ambient model reads on modeling the target surround image. Determining the black point of the display device includes modeling the target surround image as the target device with a black screen. The ambient light is included in the target surround conditions.).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Greenebaum to Aydin. The motivation would have been to enable dynamic adjustment of the display such that a viewer's perception of the displayed data remains relatively stable despite changes to the ambient conditions in which the display device is being viewed and / or to visually please the user.
Aydin in view of Greenebaum is not relied upon teaching but Vangorp teaches determining a target retinal image by modeling glare being added to the target image by a point spread function of an eye of the user (Vangorp; “To compute retinal luminance LO, we need to convolve the incoming luminance image I with a point spread function (PSF) due to the glare effect, which in this paper we call the glare spread function (GSF) O:”
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(page 4, section 4).
Vangorp; “The input signal to the adaptation mechanism must be retinal luminance and hence the first stage of our model is the optics of the eye modeled as a glare spread function (GSF, in the spatial domain) or an optical transfer function (OTF, in the Fourier domain),” (page 7, section 6).
Computing the retinal luminance image LO includes modeling glare being added to the target image. A target retinal image includes the retinal luminance image LO. The point spread function/ glare spread function relates to the eye of a human observer which reads on eye of a user. From Figure 1, it is clear that the retinal luminance image LO models glare being added to the target image (page 1, Figure 1).);
Aydin in view of Greenebaum is not relied upon teaching but Vangorp teaches calculating local adaptation pooling based on a local adaptation kernel and the target retinal image (Vangorp; “Spatial pooling may take different forms but we restricted our search to the convolution with a mixture of Gaussian functions,” (page 7, section 6).
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(page 8, section 6.2).
The spatial pooling applied to compute the adaptation luminance La comprises the local adaptation pooling. The gaussian kernels used in the spatial pooling read on the local adaptation kernel. The gaussian kernels are applied to the retinal image LO. Thus, the spatial pooling is based on the gaussian kernels and the retinal image LO.).
Aydin in view of Greenebaum is not relied upon teaching but Vangorp teaches calculating a minimum target cone response from the local adaptation pooling and the target retinal image (Vangorp; “The curve represents the smallest detectable difference in luminance when the eye is fully adapted to the luminance level L,” (page 4, section 4).
The detection threshold is the smallest detectable difference in luminance. After combination the detection threshold becomes Huang’s minimum perceptible luminance threshold and reads on the minimum target cone response.
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” where LO is the retinal image from Equation 2. The complete detection model is illustrated in Figure 6. The model predicts reasonably well our simple experiment in which the adaptation luminance La is controlled and thus approximately known (ignoring partial adaptation to the flash).” (page 5, section 5).
Δ Ldet is the detection threshold. LO is the retinal image and reads on the target retinal image. From equation 7, LO is used to calculate the detection threshold. La is the adaptation luminance.
“The detection model introduced in Section 4 should in principle predict the results of our spatial adaptation experiments from Section 5. The missing element, however, is the computation of the adaptation luminance La, shown in green in Figure 6. In this section we use our experimental data to find a model capable of predicting La,” (page 7, section 6).
“Spatial pooling may take different forms but we restricted our search to the convolution with a mixture of Gaussian functions,” (page 7, section 6).
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(page 8, section 6.2). The spatial pooling applied to compute the adaptation luminance La comprises the local adaptation pooling. The adaptation luminance La is used to calculate the detection threshold in Equation 7. Consequently, the spatial pooling applied to compute La is used to calculate the detection threshold.).
Aydin in view of Greenebaum is not relied upon teaching but Vangorp teaches determining ideal surround conditions based on the target device (“…we investigate the detection of Gabor patches of two frequencies (2 and 8 cpd) on a Gaussian pedestal of varying size of the fixed maximum luminance of 500cd/m2.The background was a uniform field of 5 cd/m2,” (Vangorp; page 5, section 5.1).
“A 9.7” Apple iPad ‘retina’ LCD panel with a resolution of 2048×1536 served as a front modulator. It ensured that the an gular resolution surpassed the maximum resolvable resolution of the eye (in excess of 240 pixels per visual degree for the viewing distance of 1.32m),” (Vangorp; page 3, section 3).
The background uniform field of 5 cd/m2 was determined for Vangorp’s apparatus: the 9.7” Apple iPad ‘retina’ LCD panel. The ideal surround conditions include the uniform field of 5 cd/m2. The target device includes Vangorp’s apparatus: the 9.7” Apple iPad ‘retina’ LCD panel. Vangorp teaches the ideal surround conditions (background uniform field of 5 cd/m2) are based on the target device/ apparatus.);
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Vangorp to Aydin in view of Greenebaum. The motivation would have been to “[derive] error bounds for physically based rendering, [determine] the backlight resolution for HDR displays, [measure] the maximum visible dynamic range in complex natural scenes, [simulate] afterimages, and gaze-dependent tone mapping,” (Vangorp; page 1, abstract) and / or to improve picture quality.
Aydin in view of Greenebaum in further view of Vangorp is not relied upon teaching but Huang teaches calculating a delta cone response under the ideal surround conditions (Huang; "…The JND is the smallest difference in the sensory input that is discernible by human being. To be specific, given a background luminance L and the corresponding just noticeable difference Δ L, the HVS cannot detect a foreground stimulus if its luminance value is between L- Δ L and L+ Δ L. The embodiment adopts a JND model proposed by Iranli et al. for low dynamic range of luminance to describe the relation between L and Δ L by
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," (pages 1-2, para [0019]-[0022]).
The JND based increment Δ L represents a delta cone response because it defines the smallest incremental change discernable by a human being and thus the cones of the eye. The ideal surround conditions include a background luminance L.);
Aydin in view of Greenebaum in further view of Vangorp is not relied upon teaching but Huang teaches calculating an adjusted target cone response by combining the minimum target cone response and the delta cone response; (Huang; "…determine a minimum perceptible luminance threshold of cone response (i.e., the response of human cone cells to luminance) with dim backlight. Below the minimum perceptible luminance threshold, detail of an image becomes invisible, therefore resulting in detail loss.," (page 1, para [0017]-[0018]).
The minimum perceptible luminance threshold of cone response reads on the minimum target cone response.
"…The JND is the smallest difference in the sensory input that is discernible by human being. To be specific, given a background luminance L and the corresponding just noticeable difference Δ L, the HVS cannot detect a foreground stimulus if its luminance value is between L- Δ L and L+ Δ L. The embodiment adopts a JND model proposed by Iranli et al. for low dynamic range of luminance to describe the relation between L and Δ L by
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," (Huang; pages 1-2, para [0019]-[0022]).The JND based increment Δ L represents a delta cone response because it defines the smallest incremental change discernable by a human being and thus the cones of the eye.
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(pages 1-2, para [0019]-[0022]). The HVS (Human visual system) response model/ function represents an adjusted cone response. The HVS response model is constructed based off establishing a lower bound, which would include the minimum perceptible luminance threshold, then adjusting the response by adding Δ L. The minimum perceptible luminance threshold and the delta cone response( Δ L) are combined in at least the equation L1 = L0 + J(L0), where the minimum perceptible luminance threshold is represented by L0 and the delta cone response represented by J(L0) since Δ L = J(L).);
Aydin in view of Greenebaum in further view of Vangorp is not relied upon teaching but Huang teaches the target luminance is calculated from the enhanced background luminance layer and the adjusted target cone response ("An enhanced luminance layer is generated through composition using the HVS response layer and the enhanced background luminance layer as inputs," (page 1, para [0009]; page 2, para [0026]).
“the HVS response layer may be obtained from the luminance layer according to the HVS response model 132,” (page 2, para [0024]).
After combination, the enhanced luminance layer comprises target luminance. The HVS (Human visual system) response model/ function represents an adjusted cone response, which is used to obtain the enhanced luminance layer.).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Huang to Aydin in view of Greenebaum in further view of Vangorp. The motivation would have been to "…[boost] luminance of image areas below a perceptual threshold while preserving contrast of other image areas," (Huang; page 1, para [0008]) and / or to improve picture quality and perceived picture quality.
Regarding claim 7, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang teaches the method of claim 1, wherein the image is an image of a video (Aydin; “FIG. 1 illustrates example operations performed in accordance with various embodiments for predicting luminance discomfort and adjusting the luminance of media content. FIG. 1 will be described in conjunction with FIG. 2, a video processing pipeline in which the luminance discomfort prediction and luminance adjustment may be implemented,” (page 2, para [0022]-[0023]).
Aydin; “At operation 104, the luminance of the media content (HDR video content in this example) may be adjusted to comport with one or more desired luminance-based effects. For example, post-processing system 208 may be utilized by a content producer to adjust the mean luminance of one or more frames in the media content that are predicted to produce luminance discomfort in viewers' visual systems,” (page 4, para [0045]). The HDR video content reads on video. The frame-by-frame processing reads on an image of a video.).
Regarding claim 8, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang teaches the method of claim 1, wherein the device is any of a movie projector, television, computer monitor, smartphone, or tablet computer (Aydin; “…luminance jumps become evident in HDR TVs and other displays capable of displaying an extended dynamic range…. Various embodiments disclosed herein provide systems and methods for assessing the level of discomfort when a sequence of images or frames is experienced on a certain display under specific viewing conditions, as well as providing mechanisms for post-processing those image sequences to ensure they remain within a desired luminance comfort zone (or zone of discomfort),” (page 2, para [0020]-[0021].). An HDR display is a component of a device.
“…a self-adjusting display, desktop, laptop, notebook, and tablet computers; hand-held computing devices (tablets, PDA's, smart phones, cell phones, palmtops, etc.); workstations or other devices with displays...,” (Aydin; page 5, para [0052]).).
Regarding claim 15, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang teaches A decoder comprising: an image data input; an adapted image data output; wherein the decoder is configured to perform the method of claim 1. (Aydin; “Such instructions embodied on the medium, are generally referred to as “computer program code” or a “computer program product” (which may be grouped in the form of computer programs or other groupings). When executed, such instructions might enable the computing component 400 to perform features or functions of the present application as discussed herein,” (page 5, para [0058]).
Aydin; “In some embodiments, the system may further comprise a post-processing system having computer code being executed to cause the post-processing system to adjust luminance of the media content based upon the one or more estimates of perceived luminance discomfort. The computer code being executed to cause the post-processing system to adjust the luminance of the media content comprises computer code that when executed, causes the post-processing system to apply a mathematical optimization function adapted to maintain a mean luminance of the media content below a luminance threshold,” (page 1, para [0010]).
The media content comprises image data input. The media content after the luminance adjustment comprises the adapted image data output. After combination, the computing component 400 is enabled to perform the methods disclosed by Aydin in view of Huang.)
Regarding claim 16, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang teaches a device comprising: a processor; a memory; wherein the memory contains code enabling the processor to perform the method of claim 1 ( Aydin; “[0053] Computing component 400 might include, for example, one or more processors, controllers, control components, or other processing devices, such as a processor 404,” (page 5, para [0052]-[0054])
“In this document, the terms “computer program medium” and “computer usable medium” are used to generally refer to transitory or non-transitory media such as, for example, memory 408, storage unit 420, media 414, and channel 428. These and other various forms of computer program media or computer usable media may be involved in carrying one or more sequences of one or more instructions to a processing device for execution. Such instructions embodied on the medium, are generally referred to as “computer program code” or a “computer program product” (which may be grouped in the form of computer programs or other groupings). When executed, such instructions might enable the computing component 400 to perform features or functions of the present application as discussed herein,” (Aydin; page 5, para [0058]). The computing component is a component of a device.).
Regarding claim 17, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang teaches a non-transient computer readable media containing code that, when read by a computer, performs the method of claim 1 (Aydin; “the terms “computer program medium” and “computer usable medium” are used to generally refer to transitory or non-transitory media such as, for example, memory 408, storage unit 420, media 414, and channel 428. These and other various forms of computer program media or computer usable media may be involved in carrying one or more sequences of one or more instructions to a processing device for execution. Such instructions embodied on the medium, are generally referred to as “computer program code” or a “computer program product” (which may be grouped in the form of computer programs or other groupings). When executed, such instructions might enable the computing component 400 to perform features or functions of the present application as discussed herein,” (page 5, para [0058]). The memory 408 and / or storage unit 420 read on non-transient computer readable media. The instructions read on code.).
Regarding claim 9, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang teaches the method of claim 1, wherein the local adaptation kernel is a convolution kernel based on pooling curves (Vangorp; “Spatial pooling may take different forms but we restricted our search to the convolution with a mixture of Gaussian functions,” (page 7, section 6).
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(Vangorp; page 8, section 6.2).
“g is a Gaussian convolution,” (Vangorp; page 7, Table 1).
The gaussian kernels /functions used in the spatial pooling read on the local adaptation kernel. The gaussian kernels /functions are used in convolution operations and are convolution kernels.
“To measure the extent of the visual area that influences the adaptation luminance, edge targets were displayed on a disk-shaped pedestal of 2500 or 50 cd=m2 of a variable diameter, on a background of 5 cd=m2. Figure 8 shows that the detection threshold, and hence the adaptation luminance, levels off around a diameter of 0.5° of visual angle,” (Vangorp; page 5, section 5.2).
“The two previous experiments measured pooling as a function of distance from the fixation point. However, they cannot explain what kind of non-linearity is involved: pooling might occur in linear (luminance) space, in logarithmic space, or in any other non-linear space. To determine this non-linearity, the stimulus was flanked by a concentric half or full ring of 1° outer diameter. The luminance of this ring varied from 0.5 to 5000 cd=m2. The half ring was cut diagonally to reduce any possible interference with the vertical or horizontal detection target. The background was fixed at 0.5 cd=m2,” (Vangorp; page 6, section 5.4).
“The detection model introduced in Section 4 should in principle predict the results of our spatial adaptation experiments from Section 5. The missing element, however, is the computation of the adaptation luminance La, shown in green in Figure 6. In this section we use our experimental data to find a model capable of predicting La,” (Vangorp; page 7, section 6).
“Spatial pooling may take different forms but we restricted our search to the convolution with a mixture of Gaussian functions. However, we allowed each term of the Gaussian mixture to be optionally preceded by one of several non-linearities: logarithmic (a common approximation of the receptor response), a power function with an exponent as a free parameter, or a custom non-linearity designed as a monotonic, C1-continuous function created from a cubic interpolation of four nodes, where the position of each node was a free parameter. Each non-linearity was paired with its inverse applied after Gaussian convolution. The schematic diagram of possible model combinations is shown in Figure 15,” (Vangorp; page 7, section 6)
“Given all unique combinations of model components, we generated 56 candidate local adaptation models and fitted each separately to the results of Section 5,” (Vangorp; page 7, section 6).
Section 5 describes experiments to measure spatial pooling including determining non-linearity which reads on pooling curves. Section 6 describes using the results of the spatial adaptation experiments from section 5, which would include the pooling curves, to compute the adaptation luminance in equation 10. Equation 10 uses gaussian kernels whose parameters are based on the pooling curves. ).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Vangorp to Aydin in view of Greenebaum in further view of Huang. The motivation would have been to “[derive] error bounds for physically based rendering, [determine] the backlight resolution for HDR displays, [measure] the maximum visible dynamic range in complex natural scenes, [simulate] afterimages, and gaze-dependent tone mapping,” (Vangorp; page 1, abstract) and / or to improve picture quality.
Regarding claim 11, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang teaches the method of claim 1, wherein the target surround conditions include two or more of: the type of screen the user is looking at, the viewing angle between the user and the screen, the distance from the user to the screen, and a flat field description of the ambient luminance conditions (Vangorp; “First, we simulated viewing 8 standard images on a 40" HDR display of unrestricted brightness and dynamic range, from the viewing distance of 3 image heights (recommended for an HD resolution).” The 40" HDR display of unrestricted brightness and dynamic range reads on the type of screen the user is looking at. The viewing distance of 3 image heights reads on the distance from the user to the screen.).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Vangorp to Aydin in view of Greenebaum in further view of Huang. The motivation would have been to “[derive] error bounds for physically based rendering, [determine] the backlight resolution for HDR displays, [measure] the maximum visible dynamic range in complex natural scenes, [simulate] afterimages, and gaze-dependent tone mapping,” (Vangorp; page 1, abstract) and / or improve perceived image quality for real-world scenes.
Regarding claim 5, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang teaches the method of claim 1, wherein the modeling the target surround image comprises modeling the target device with a black screen and with target surround conditions (Greenebaum; “In one embodiment, the optical sensor 404 may comprise a video camera capable of capturing spatial information, color information, and intensity information. Thus, utilizing a video camera could allow for the creation of an ambient model that could adapt not only the gamma and black point of the display device, but also the display device's white point,” (page 6, para [0050]).
“In one embodiment, the black level for a given ambient environment may be determined, e.g., by using an ambient light sensor 404 or by taking measurements from the display device's actual panel and/or diffuser. As mentioned above in reference to FIG. 4, diffuse reflection of ambient light off the surface of the device may cause a certain range of the darkest display levels to become indiscernible to the viewer. Generally, ambient light as reflected off the display, as well as backlight that is not stopped by the display at the blackest values combine additively to create a so-called “pedestal.” Pedestals does not technically mask display values but rather make all displayed values brighter by the “pedestal” amount. Stated more directly, ambient light as viewed reflected off surfaces changes the user's adaptation… Once this level of diffuse reflection is determined, the black point may be adjusted accordingly. For example, if all luminance values below an 8-bit value of 40 would be indiscernible to the viewer over the level of diffuse reflection (though this is likely an extreme example), the system 700 may set the black point to be 40, thus compressing the pixel luminance values into the range of 41-255.,” (Greenebaum; pages 8-9, para [0065]).
Optical sensor 404 comprises ambient light sensor 404. The creation of the ambient model reads on modeling the target surround image. Determining the black point of the display device includes modeling the target surround image as the target device with a black screen. The ambient light is included in the target surround conditions.).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Greenebaum to Aydin in view of Huang in further view of Vangorp. The motivation would have been to enable dynamic adjustment of the display such that a viewer's perception of the displayed data remains relatively stable despite changes to the ambient conditions in which the display device is being viewed and / or to visually please the user.
Regarding claim 12, Aydin teaches a method for adjusting a target image to be viewed by a user comprising: ("...However, content that is filmed in HDR and/or presented on an HDR display may have downsides associated with the extended dynamic range. For example, a viewer's visual system may become strained during abrupt transitions from dark frames of content to much brighter frames of content. This can lead to viewing discomfort.... Further still, the computer-implemented method comprises adjusting luminance of the media content to comport with one or more desired luminance-based effects. In one aspect, the analyzing of the media content comprises determining a luminance level associated with a pixel of a frame of the media content. In another aspect, the analyzing of the media content comprises determining a luminance level associated with a spatial neighborhood approximately about the pixel. In still another aspect, the analyzing of the media content comprises determining an ambient luminance level relative to the pixel," (page 1, para [0002] - [0003]; page 2, para [0020]).
“It should be noted that ambient luminance can refer to lighting other than that emanating from a display or screen on which content is presented. This can include, for example, ceiling lights, lamps, or other light sources in a room where a display is located,” (page 3, para [0030]).
Adjusting luminance of the media content reads on adjusting a target image. A viewer's visual system reads on to be viewed by a user.)
calculating a target luminance from the "In some embodiments, the computer-implemented method further comprises applying a pooling function to combine the one or more corresponding levels of perceived luminance discomfort associated with determined luminance levels of one or more pixels of a frame of the media content, the combination of the one or more corresponding levels of perceived luminance discomfort comprising a frame-wide estimate of perceived luminance discomfort. Each of the one or more corresponding levels of perceived luminance discomfort comprises a subjective determination of discomfort experienced during exposure to test media content having commensurate luminance characteristics as the analyzed media content," (page 1, para [0005])
"Accordingly, some embodiments of the present disclosure may implement a “pooling function” to avoid analyzing content in a manner that is overly granular. For example, a frame of video content may contain a subset of pixels representative of a relatively small spotlight that does not impact a viewer's perception of the overall luminance of that frame. A pooling function can be utilized to adapt the maladaptation model for use with some larger subset of pixels to get a more accurate representation of luminance in the frame," (page 3, para [0035]).
“It should be understood that the above transducer function incorporates a mapping function from {circumflex over (L)}.sup.t to {circumflex over (D)}.sup.t that minimizes ∥D.sup.t−D.sup.t∥.sup.2 over all the obtained subjective data to achieve data that is normalized/improve data integrity. That is, transducer function τ is designed to minimize the difference of test data D.sup.t to predicted luminance discomfort D.sup.t. Additionally, for practical reasons, as noted above, transfer function τ can be defined per-frame (or some other subset) rather than per-pixel. Thus, the aforementioned pooling function may combine per-pixel luminance discomfort estimates or predictions into frame-wide luminance discomfort estimates or predictions,” (page 4, para [0044]).
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(page 3, para [0029]).
"In some embodiments, the system may further comprise a post-processing system having computer code being executed to cause the post-processing system to adjust luminance of the media content based upon the one or more estimates of perceived luminance discomfort," (page 1, para [0010]; page 4, para [0045] - [0047]).
“…a director may utilize post-processing system 208 to apply a mathematical optimization function to adjust the mean luminance of an entire movie to ensure that a perceived luminance discomfort level of 3 is never exceeded,” (page 4, para [0045] - [0047]).
The adjusted mean luminance (Lt) reads on the target luminance. The pooling function is used to determine the perceived luminance discomfort which in turn is used to calculate the luminance adjustment. The pooling function represents a local adaptation pooling because the pooling function includes per-pixel predictions/ adaptations and spatial grouping comprising the subset of pixels representative of a relatively small spotlight. The disclosed pooling function is consistent with the definition given in the specification (specification, para [0021]).);
generating an adjusted image based on the target luminance. ("In some embodiments, the system may further comprise a post-processing system having computer code being executed to cause the post-processing system to adjust luminance of the media content based upon the one or more estimates of perceived luminance discomfort," (page 1, para [0010]; page 4, para [0045] - [0047]).
“some embodiments may further rely on the perceived luminance discomfort to adjust the mean luminance, Lt, of each video frame,” (page 4, para [0046]).
“…a director may utilize post-processing system 208 to apply a mathematical optimization function to adjust the mean luminance of an entire movie to ensure that a perceived luminance discomfort level of 3 is never exceeded,” (page 4, para [0045] - [0047]).
Adjusting the luminance of the media content reads on generating an adjusted image. Aydin is adjusting the luminance of the media content based on the adjusted mean luminance (Lt) (reads on target luminance). The media content after the luminance adjustment is the adapted image.).
Aydin is not relied upon teaching but Greenebaum teaches modeling an ideal surround with the target image (Greenebaum; “The overall goal of some color adaptation models may be to understand how the source material is ideally intended to “look” on a viewer's display. In a typical scenario for video, the ideal viewing conditions may be modeled as a broadcast monitor in a dim broadcast studio environment lit by 16 lux of CIE Standard Illuminant D65 light. This source rendering intent may be modeled, e.g., by attaching an ICC profile to the source. The attachment of a profile to the source data may allow the display device to interpret and render the content according to the source creator's “rendering intent.” Once the rendering intent has been determined, the display device may determine how to transform the source content to make it match the ideal appearance on the display device, which may (and likely will) be a non-broadcast monitor, in an environment lit by non-D65 light, and with something other than 16 lux ambient lighting,” (pages 9-10; para [0070]).
Modeling an ideal surround includes modeling ideal viewing conditions. Modeling with the target image includes modeling by attaching an ICC profile to the source. The target image includes the source / source data / source content.);
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Greenebaum to Aydin. The motivation would have been to enable dynamic adjustment of the display such that a viewer's perception of the displayed data remains relatively stable despite changes to the ambient conditions in which the display device is being viewed. Additional motivation would have been to visually please the user.
Aydin in view of Greenebaum is not relied upon teaching but Vangorp teaches determining a target retinal image by modeling glare added to the target image using a point spread function of an eye of the user (Vangorp; “To compute retinal luminance LO, we need to convolve the incoming luminance image I with a point spread function (PSF) due to the glare effect, which in this paper we call the glare spread function (GSF) O:”
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(page 4, section 4).
Vangorp; “The input signal to the adaptation mechanism must be retinal luminance and hence the first stage of our model is the optics of the eye modeled as a glare spread function (GSF, in the spatial domain) or an optical transfer function (OTF, in the Fourier domain),” (page 7, section 6).
Computing the retinal luminance image LO includes modeling glare being added to the target image. The point spread function/ glare spread function relates to the eye of a human observer which reads on eye of a user. From Figure 1, it is clear that the retinal luminance image LO models glare being added to the target image (page 1, Figure 1).
Computing the retinal luminance image LO includes modeling glare being added to the target image. A target retinal image includes the retinal luminance image LO. The point spread function/ glare spread function relates to the eye of a human observer which reads on eye of a user. From Figure 1, it is clear that the retinal luminance image LO models glare being added to the target image (page 1, Figure 1).);
Aydin in view of Greenebaum is not relied upon teaching but Vangorp teaches calculating local adaptation pooling from a local adaptation kernel and the ideal retinal image (Vangorp; “Spatial pooling may take different forms but we restricted our search to the convolution with a mixture of Gaussian functions,” (page 7, section 6).
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(page 8, section 6.2).
The spatial pooling applied to compute the adaptation luminance La comprises the local adaptation pooling. The gaussian kernels used in the spatial pooling read on the local adaptation kernel. The gaussian kernels are applied to the retinal image LO. The ideal retinal image includes the retinal image LO. The spatial pooling is calculated from the gaussian kernels and the retinal image LO. The examiner notes there is nothing in the specification that distinguishes a target retinal image from an ideal retinal image.);
Aydin in view of Greenebaum is not relied upon teaching but Vangorp teaches calculating a minimum target cone response from the local adaptation pooling and the target retinal image (Vangorp; “The curve represents the smallest detectable difference in luminance when the eye is fully adapted to the luminance level L,” (page 4, section 4).
The detection threshold is the smallest detectable difference in luminance. After combination the detection threshold becomes Huang’s minimum perceptible luminance threshold and reads on the minimum target cone response.
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” where LO is the retinal image from Equation 2. The complete detection model is illustrated in Figure 6. The model predicts reasonably well our simple experiment in which the adaptation luminance La is controlled and thus approximately known (ignoring partial adaptation to the flash).” (page 5, section 5).
Δ Ldet is the detection threshold. LO is the retinal image and reads on the target retinal image. From equation 7, LO is used to calculate the detection threshold. La is the adaptation luminance.
“The detection model introduced in Section 4 should in principle predict the results of our spatial adaptation experiments from Section 5. The missing element, however, is the computation of the adaptation luminance La, shown in green in Figure 6. In this section we use our experimental data to find a model capable of predicting La,” (page 7, section 6).
“Spatial pooling may take different forms but we restricted our search to the convolution with a mixture of Gaussian functions,” (page 7, section 6).
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(page 8, section 6.2). The spatial pooling applied to compute the adaptation luminance La comprises the local adaptation pooling. The adaptation luminance La is used to calculate the detection threshold in Equation 7. Consequently, the spatial pooling applied to compute La is used to calculate the detection threshold.).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Vangorp to Aydin in view of Greenebaum. The motivation would have been to “[derive] error bounds for physically based rendering, [determine] the backlight resolution for HDR displays, [measure] the maximum visible dynamic range in complex natural scenes, [simulate] afterimages, and gaze-dependent tone mapping,” (Vangorp; page 1, abstract) and / or to improve picture quality.
Aydin in view of Greenebaum in further view of Vangorp is not relied upon teaching but Huang teaches determining a delta cone response based on ideal surround conditions (Huang; "…The JND is the smallest difference in the sensory input that is discernible by human being. To be specific, given a background luminance L and the corresponding just noticeable difference Δ L, the HVS cannot detect a foreground stimulus if its luminance value is between L- Δ L and L+ Δ L. The embodiment adopts a JND model proposed by Iranli et al. for low dynamic range of luminance to describe the relation between L and Δ L by
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," (pages 1-2, para [0019]-[0022]).
The JND based increment Δ L represents a delta cone response because it defines the smallest incremental change discernable by a human being and thus the cones of the eye. The ideal surround conditions include a background luminance L.);
Aydin in view of Greenebaum in further view of Vangorp is not relied upon teaching but Huang teaches calculating an adjusted target cone response by combining the minimum target cone response and the delta cone response; (Huang; "…determine a minimum perceptible luminance threshold of cone response (i.e., the response of human cone cells to luminance) with dim backlight. Below the minimum perceptible luminance threshold, detail of an image becomes invisible, therefore resulting in detail loss.," (page 1, para [0017]-[0018]).
The minimum perceptible luminance threshold of cone response reads on the minimum target cone response.
"…The JND is the smallest difference in the sensory input that is discernible by human being. To be specific, given a background luminance L and the corresponding just noticeable difference Δ L, the HVS cannot detect a foreground stimulus if its luminance value is between L- Δ L and L+ Δ L. The embodiment adopts a JND model proposed by Iranli et al. for low dynamic range of luminance to describe the relation between L and Δ L by
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," (Huang; pages 1-2, para [0019]-[0022]).The JND based increment Δ L represents a delta cone response because it defines the smallest incremental change discernable by a human being and thus the cones of the eye.
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(pages 1-2, para [0019]-[0022]). The HVS (Human visual system) response model/ function represents an adjusted cone response. The HVS response model is constructed based off establishing a lower bound, which would include the minimum perceptible luminance threshold, then adjusting the response by adding Δ L. The minimum perceptible luminance threshold and the delta cone response( Δ L) are combined in at least the equation L1 = L0 + J(L0), where the minimum perceptible luminance threshold is represented by L0 and the delta cone response represented by J(L0) since Δ L = J(L).);
Aydin in view of Greenebaum in further view of Vangorp is not relied upon teaching but Huang teaches the target luminance is calculated from the adjusted target cone response and the enhanced background luminance layer ("An enhanced luminance layer is generated through composition using the HVS response layer and the enhanced background luminance layer as inputs," (page 1, para [0009]; page 2, para [0026]).
“the HVS response layer may be obtained from the luminance layer according to the HVS response model 132,” (page 2, para [0024]).
After combination, the enhanced luminance layer comprises target luminance. The HVS (Human visual system) response model/ function represents an adjusted cone response, which is used to obtain the enhanced luminance layer.).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Huang to Aydin in view of Greenebaum in further view of Vangorp. The motivation would have been to "…[boost] luminance of image areas below a perceptual threshold while preserving contrast of other image areas," (Huang; page 1, para [0008]) and / or to improve picture quality and perceived picture quality.
Regarding claim 14, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang teaches the method of claim 12, wherein the adjusted cone response is calculated as an absolute cone response value (For the purpose of compact prosecution and art rejection, the examiner will interpret the term “adjusted cone response value” to mean the adjusted cone response is calculated and a result is used as a cone response value.
Huang; "…determine a minimum perceptible luminance threshold of cone response (i.e., the response of human cone cells to luminance) with dim backlight. Below the minimum perceptible luminance threshold, detail of an image becomes invisible, therefore resulting in detail loss.," (page 1, para [0017]-[0018]).
Huang; "…The JND is the smallest difference in the sensory input that is discernible by human being. To be specific, given a background luminance L and the corresponding just noticeable difference Δ L, the HVS cannot detect a foreground stimulus if its luminance value is between L- Δ L and L+ Δ L…," (pages 1-2, para [0019]-[0022]).
The JND based increment Δ L defines the smallest incremental change discernable by a human being and thus the cones of the eye.
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( Huang; pages 1-2, para [0019]-[0022]).
Huang; “the HVS response model may characterize this nonlinear behavior by taking the luminance value as an input and converting it to a nonnegative integer as an output such that a difference of 1 in the output corresponds to a just noticeable difference (JND) in luminance,” (pages 1-2, para [0019]-[0022]).
The HVS (Human visual system) response model/ function represents an adjusted cone response. The HVS response model is calculated based off establishing a lower bound, which would include the minimum perceptible luminance threshold of cone response, then adjusting the cone response by adding Δ L. The HVS response output/ value reads on the absolute cone response value because it is used as the cone response value to change the luminance.).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Huang to Aydin in view of Greenebaum in further view of Vangorp. The motivation would have been to "…[boost] luminance of image areas below a perceptual threshold while preserving contrast of other image areas," (Huang; page 1, para [0008]). An additional motivation would have been to improve picture quality and perceived picture quality.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Aydin in view of Greenebaum in further view of Vangorp in further view of Huang in further view of Talvala et al. (Talvala et al., "Veiling Glare In High Dynamic Range Imaging," July 2007, Association for Computing Machinery, Vol 26, pages 1-10; hereinafter Talvala).
Regarding claim 6, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang is not relied upon teaching but Talvala teaches the method of claim 1, further comprising: subtracting the target retinal image from the adapted image (Talvala; “Our second method places a flat occluder between the camera and scene, perforated with holes. The regions of the sensor that image through holes record the direct component of light, plus the veiling glare. Adjacent sensor regions that image only the occluder record only glare from the adjacent holes. We form an estimate for the glare in the holes by interpolating the glare values from occluded regions across the holes. Then we subtract the glare estimate from the image, resulting in a glare-free estimate for the hole regions,” (page 2, Section 1).
“We then interpolate ˆ gφ across the unoccluded regions using a weighted Gaussian blur with standard deviation of one third of the mask pattern period. If f is the Gaussian blur kernel, then
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where the division is elementwise. This gives us a low-frequency glare estimate gφ for the entire capture. Next, we subtract the estimated glare from the original captured image, and discard the regions outside the mask holes to create a glare-free estimate for this capture, sφ,” (pages 6-7, section 5.2).
The glare estimate gφ reads on the target retinal image. The mapping is consistent with the specification which describes the “retinal image” as a model of a type of light (for example glare) with impact on a person’s eye (specification, para [0034]). The glare estimate is subtracted from an image. After combination, the image becomes the adapted image taught by Aydin in view of Huang.).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Talvala to Aydin in view of Greenebaum in further view of Vangorp in further view of Huang. The motivation would have been to improve contrast of the image and / or to improve perceived image quality.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Aydin in view of Greenebaum in further view of Vangorp in further view of Huang in further view of Arjmand et al. (US 20250200960 A1; hereinafter Arjmand).
Regarding claim 10, Aydin in view of Greenebaum in further view of Vangorp in further view of Huang is not relied upon teaching but Arjmand teaches the method of claim 1, wherein the local adaptation kernel is a 10x10 kernel (Arjmand; “…the kernel size may be 10-by-10 pixels,” (page 1,para [0006]).
Arjmand; “…the kernel is a Gaussian kernel,” (para [0015).
After combination, Vangorp’s gaussian kernels become 10-by-10 gaussian kernels.).
Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Arjmand to Aydin in view of Greenebaum in further view of Vangorp in further view of Huang. The motivation would have been to achieve an appropriate balance between retaining detail and filtering.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/ERICA G THERKORN/Examiner, Art Unit 2618
/DEVONA E FAULK/Supervisory Patent Examiner, Art Unit 2618
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