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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/06/2023 has been considered by the examiner and placed in applicant file.
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 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 of this title, 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 1-3, 7-10, 15 and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over WANG et al. (US 20210321020 A1), hereinafter referenced as WANG in view of XU et al. (US 20160360202 A1), hereinafter referenced as XU and in further view of SU et al. (US 20160021391 A1), hereinafter referenced as SU.
Regarding claim 1, WANG explicitly teaches a method (Fig. 5. Paragraph [0016]-WANG discloses aspects of the disclosure generally relate to automated detection and assessment of visual banding effect in digital images and video signals. Further in paragraph [0017]-WANG discloses another aspect of the present disclosure relates to creating a banding map. Yet another aspect of the present disclosure relates to creating a banding spread measure and a banding strength measure. Yet another aspect of the present disclosure relates to banding measurement of a video, where the per-frame banding spread, per-frame banding strength and per-frame banding level (or banding presence) measures are aggregated over a time scale. Please also see Fig. 2-4 and 6-7), comprising:
receiving an input image having a first dynamic range (Fig. 5. Paragraph [0018]-WANG discloses the present disclosure applies to both standard dynamic range (SDR) and high dynamic range (HDR) images and videos. In paragraph [0022]-WANG discloses the banding effect may be detected and assessed in HDR images/videos (wherein High dynamic range images/videos have a first dynamic range));
Although WANG explicitly teaches processing the input image to generate a band size map that identifies at least one banding artifact in the input image (Fig. 6. Paragraph [0022]-WANG discloses the detection and assessment of the banding effect may be determined from an image or video clip at the pixel level. Further in paragraph [0042]-WANG discloses FIG. 6 illustrates an example method for generating banding maps. The banding map may be created for an image or video frame from the detected banding pixels. This process results in a binary map of the same size as the image or video frame being assessed, where the positions of detected banding pixels are marked as a first value (e.g., 1, true, etc.), and the rest of the map is marked as a second value (e.g., 0, false, etc.). This banding map reveals how much the banding effect is visible at each spatial location in an image or video frame. Further in paragraph [0043]-WANG discloses the detected banding pixels at each image or video frame may be aggregated to several banding assessment measurement, including per-channel and per-frame banding spread, banding strength, and banding level measures. Please also read paragraph [0019 and 0040-0041]).
WANG fails to explicitly teach processing, using a neural network, the input image to generate a band size map.
However, XU explicitly teaches processing, using a neural network, the input image to generate a band size map (Fig. 1. Paragraph [0062]-XU discloses regarding the architecture of the feed-forward neural network 34, given the features extracted from each image block, a the feed-forward neural network 34 is trained, e.g., via the training module 40, to learn a classifier that maps a feature vector to a binary value, e.g., 0 or 1, where 0 represents “no banding” and 1 represents “banding.” (wherein stochastic training and optimization and multiple filters, including a smoothing filter may be used)).
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 WANG of having a method, comprising: receiving an input image having a first dynamic range, with the teachings of XU of having processing, using a neural network, the input image to generate a band size map.
Wherein having WANG’s method having processing, using a neural network, the input image to generate a band size map that identifies at least one banding artifact in the input image
The motivation behind the modification would have been to obtain a method that improves the accuracy and efficiency of banding artifact detection and removal, since both WANG and XU systems and methods for image and banding artifact analysis. Wherein WANG provides systems and methods for automated detection, assessment and removal of visual banding effect in digital images and video signals that balances accuracy and efficiency, while XU provides systems and methods that improve the accuracy of banding prediction based on color and gradient features in YCbCr color spaces and use of a feed-forward neural network classifier 34. Please see WANG et al. (US 20210321020 A1), Abstract and Paragraph [0010] and XU et al. (US 20160360202 A1), Abstract and Paragraph [0069].
WANG in view of XU fails to explicitly teach generating, using a blur filter, a de-banded image based on the band size map and the input image, wherein the blur filter removes at least a portion of the at least one banding artifact from the input image; and generating, based on the de-banded image, an output image having a second dynamic range that is greater than the first dynamic range.
However, SU explicitly teaches generating, using a blur filter, a de-banded image based on the band size map (Fig. 1. Paragraph [0089]-SU discloses in block 302, image areas with a high confidence level to have false contouring are determined in an image (e.g., a predicted high bit depth image, etc.). Image areas computed with the largest possible N in Ω represent high confidence image areas (or collectively a confidence area in short) for flat areas (e.g., having false contouring, etc.). Please also read paragraph [0056-0060 and 0099-0103]) and the input image (Fig. 1. Paragraph [0055]-SU discloses an input image 101 (e.g., a VDR image, a predicted high bit depth image predicted from a low bit depth image, a decoded image that comprises banding artifacts, etc.) to which the decontouring operations are to be performed is denoted as v) wherein the blur filter removes at least a portion of the at least one banding artifact from the input image (Fig. 1. Paragraph [0067]-SU discloses a blur filter can be used at least as a part of the smoothen operator to remove banding artifacts in a high bit depth image (e.g., that is predicted from a low bit depth image that lost original real values in quantization, etc.). A convolution of a 2-D (separable or non-separable) low pass kernel, etc., may be used to generate smooth area over flat areas or stair wise areas); and
generating, based on the de-banded image, an output image having a second dynamic range that is greater than the first dynamic range (Fig. 1. Paragraph [0119]-SU discloses the smoothen operator (410) is applied to the prediction image data (e.g., intermediate high dynamic range image, etc.) to generate smoothened prediction image data (e.g., a smoothened high bit depth image, etc.). Further in paragraph [0148]-SU discloses in block 502, a multi-layer video encoder (e.g., 402 of FIG. 4A) determines flat image areas in a predicted image, the predicted image being predicted from a first image mapped from a second image that has a higher dynamic range than the first image. Please also read paragraph [0122-0123, 0129, 0133 and 0139-0140]).
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 WANG in view of XU of having a method, comprising: receiving an input image having a first dynamic range with the teachings of SU of having generating, using a blur filter, a de-banded image based on the band size map and the input image, wherein the blur filter removes at least a portion of the at least one banding artifact from the input image; and generating, based on the de-banded image, an output image having a second dynamic range that is greater than the first dynamic range.
Wherein having WANG’s method having generating, using a blur filter, a de-banded image based on the band size map and the input image, wherein the blur filter removes at least a portion of the at least one banding artifact from the input image; and generating, based on the de-banded image, an output image having a second dynamic range that is greater than the first dynamic range.
The motivation behind the modification would have been to obtain a method that improves the accuracy and efficiency of banding artifact detection and removal, since both WANG and SU systems and methods for image analysis in the context of banding artifacts. Wherein WANG provides systems and methods for automated detection, assessment and removal of visual banding effect in digital images and video signals that balances accuracy and efficiency, while SU provides systems and methods that provide optimal operational parameters for removing or reducing banding artifacts with relatively small distortion and improve bit depth expansion in the false contouring areas (or stair wise areas) significantly both perceptually and objectively. Please see WANG (US 20210321020 A1), Abstract and Paragraph [0010] and SU (US 20160021391 A1), Abstract and Paragraph [0031, 0084 and 0087].
Regarding claim 2, WANG in view of XU and in further view of SU explicitly teach the method of claim 1, WANG further teaches wherein the band size map identifies at least one of a predicted band size or a predicted pixel location of the at least one banding artifact in the image (Fig. 6. Paragraph [0022] -WANG discloses the detection and assessment of the banding effect may be determined from an image or video clip at the pixel level. Further in paragraph [0042]-WANG discloses FIG. 6 illustrates an example method for generating banding maps. The banding map may be created for an image or video frame from the detected banding pixels. This process results in a binary map of the same size as the image or video frame being assessed, where the positions of detected banding pixels are marked as a first value (e.g., 1, true, etc.), and the rest of the map is marked as a second value (e.g., 0, false, etc.). This banding map reveals how much the banding effect is visible at each spatial location in an image or video frame. Further in paragraph [0043]-WANG discloses the detected banding pixels at each image or video frame may be aggregated to several banding assessment measurement, including per-channel and per-frame banding spread, banding strength, and banding level measures. Please also read paragraph [0019 and 0040-0041]).
Regarding claim 3, WANG in view of XU and in further view of SU explicitly teach the method of claim 2, WANG further teaches wherein the band size map includes a plurality of channels (Fig. 6. Paragraph [0024]-WANG discloses at operation 202, the image or each video frame are decomposed into luminance or color channels. In paragraph [0025]-WANG discloses at operation 204, the signal activity is measured at each spatial location. In paragraph [0026]-WANG discloses at operation 206, banding pixels 208 are determined. Additionally in paragraph [0042]-WANG discloses the banding map may be created for an image or video frame from the detected banding pixels. This process results in a binary map of the same size as the image or video frame being assessed, where the positions of detected banding pixels are marked as a first value (e.g., 1, true, etc.), and the rest of the map is marked as a second value (e.g., 0, false, etc.) (wherein the signal activity of banding maps is normalized based on the significance threshold at operation 606, and both the collection of detected banding pixels and banding map may be aggregated with other measures such as per color/per frame banding). Please also see paragraph [0044-0046]), wherein at least one channel of the plurality of channels corresponds to a respective threshold band size in a plurality of threshold band sizes (Fig. 7. Paragraph [0045]-WANG discloses based on the banding map of a color channel, a per-channel banding spread is computed at operation 706 by computing the percentage of areas in the image or video frame that contain banding pixels. Based on the banding map, a per-channel banding strength is computed 708 by averaging the signal activities at all banding pixels. Further in paragraph [0046]-WANG discloses the resulting per-channel banding spread 710 and per-channel banding strength 712, are then combined to an overall banding level measure of the channel at operation 714. The combination may be performed by computing their sum, average, product, weighted-average, median, maximum, minimum, or other methods. Moreover, in paragraph [0047]-WANG discloses the per-channel banding level measures are combined into a single per-frame banding level measure at operation 718. The combination may be conducted by computing the sum, average, product, weighted-average, median, maximum, or minimum of per-channel banding level measures).
Regarding claim 7, WANG in view of XU and in further view of SU explicitly teach the method of claim 1, WANG in view of XU fail to explicitly teach wherein the blur filter removes the at least a portion of the at least one banding artifact from an area of the input image, wherein the area of the input image is identified based at least in part on at least one of the predicted pixel location or a particular threshold band size that corresponds to a channel of the band size map.
However, SU explicitly teaches wherein the blur filter removes the at least a portion of the at least one banding artifact from an area of the input image, wherein the area of the input image is identified based at least in part on at least one of the predicted pixel location or a particular threshold band size that corresponds to a channel of the band size map (Fig. 1. Paragraph [0067]-SU discloses a blur filter can be used at least as a part of the smoothen operator to remove banding artifacts in a high bit depth image (e.g., that is predicted from a low bit depth image that lost original real values in quantization, etc.). A convolution of a 2-D (separable or non-separable) low pass kernel, etc., may be used to generate smooth area over flat areas or stair wise areas).
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 WANG in view of XU and in further view of SU of having a method, comprising: receiving an input image having a first dynamic range, with the teachings of SU of having wherein the blur filter removes the at least a portion of the at least one banding artifact from an area of the input image, wherein the area of the input image is identified based at least in part on at least one of the predicted pixel location or a particular threshold band size that corresponds to a channel of the band size map.
Wherein having WANG’s method having wherein the blur filter removes the at least a portion of the at least one banding artifact from an area of the input image, wherein the area of the input image is identified based at least in part on at least one of the predicted pixel location or a particular threshold band size that corresponds to a channel of the band size map.
The motivation behind the modification would have been to obtain a method that improves the accuracy and efficiency of banding artifact detection and removal, since both WANG and SU systems and methods for image analysis in the context of banding artifacts. Wherein WANG provides systems and methods for automated detection, assessment and removal of visual banding effect in digital images and video signals that balances accuracy and efficiency, while SU provides systems and methods that provide optimal operational parameters for removing or reducing banding artifacts with relatively small distortion and improve bit depth expansion in the false contouring areas (or stair wise areas) significantly both perceptually and objectively. Please see WANG (US 20210321020 A1), Abstract and Paragraph [0010] and SU (US 20160021391 A1), Abstract and Paragraph [0031, 0084 and 0087].
Regarding claim 8, WANG in view of XU and in further view of SU explicitly teach the method of claim 7, WANG in view of XU fail to explicitly teach wherein the blur filter uses at least one of the predicted pixel location or the particular threshold band size to remove the respective band.
However, SU explicitly teaches wherein the blur filter uses at least one of the predicted pixel location or the particular threshold band size to remove the respective band (Fig. 1. Paragraph [0067]-SU discloses a blur filter can be used at least as a part of the smoothen operator to remove banding artifacts in a high bit depth image (e.g., that is predicted from a low bit depth image that lost original real values in quantization, etc.). A convolution of a 2-D (separable or non-separable) low pass kernel, etc., may be used to generate smooth area over flat areas or stair wise areas).
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 WANG in view of XU and in further view of SU of having a method, comprising: receiving an input image having a first dynamic range, with the teachings of SU of having wherein the blur filter uses at least one of the predicted pixel location or the particular threshold band size to remove the respective band.
Wherein having WANG’s method having wherein the blur filter uses at least one of the predicted pixel location or the particular threshold band size to remove the respective band.
The motivation behind the modification would have been to obtain a method that improves the accuracy and efficiency of banding artifact detection and removal, since both WANG and SU systems and methods for image analysis in the context of banding artifacts. Wherein WANG provides systems and methods for automated detection, assessment and removal of visual banding effect in digital images and video signals that balances accuracy and efficiency, while SU provides systems and methods that provide optimal operational parameters for removing or reducing banding artifacts with relatively small distortion and improve bit depth expansion in the false contouring areas (or stair wise areas) significantly both perceptually and objectively. Please see WANG (US 20210321020 A1), Abstract and Paragraph [0010] and SU (US 20160021391 A1), Abstract and Paragraph [0031, 0084 and 0087].
Regarding claim 9, WANG in view of XU and in further view of SU explicitly teach the method of claim 1, WANG further teaches wherein the band size map (Fig. 6. Paragraph [0042]-WANG discloses the banding map may be created for an image or video frame from the detected banding pixels. This process results in a binary map of the same size as the image or video frame being assessed, where the positions of detected banding pixels are marked as a first value (e.g., 1, true, etc.), and the rest of the map is marked as a second value (e.g., 0, false, etc.) (wherein the banding map may be used and/or aggregated with other measures such as the collection of detected banding pixel operations, per color/per frame banding). Please also see paragraph [0044-0046]) includes a plurality of scalar band size values, wherein each scalar band size value in the plurality of scalar band size values specifies a respective predicted size of a respective corresponding band (Fig. 7. Paragraph [0045]-WANG discloses based on the banding map of a color channel, a per-channel banding spread is computed at operation 706. Based on the banding map, a per-channel banding strength is computed 708. Further in paragraph [0046]-WANG discloses the resulting per-channel banding spread 710 and per-channel banding strength 712, are then combined to an overall banding level measure of the channel at operation 714. Moreover, in paragraph [0047]-WANG discloses the per-channel banding level measures are combined into a single per-frame banding level measure at operation 718. The combination may be conducted by computing the sum, average, product, weighted-average, median, maximum, or minimum of per-channel banding level measures. The resulting banding level measure 720 is a scalar banding assessment result for the image or video frame).
Regarding claim 10, WANG in view of XU and in further view of SU explicitly teach the method of claim 9, WANG further teaches wherein each respective corresponding band includes a pixel to which the scalar band size value corresponds in the band size map (Fig. 6. Paragraph [0024]-WANG discloses at operation 202, the image or each video frame are decomposed into luminance or color channels. In paragraph [0026]-WANG discloses at operation 206, banding pixels 208 are determined. In paragraph [0042]-WANG discloses the banding map may be created for an image or video frame from the detected banding pixels. This process results in a binary map of the same size as the image or video frame being assessed, where the positions of detected banding pixels are marked as a first value (e.g., 1, true, etc.), and the rest of the map is marked as a second value (e.g., 0, false, etc.). In paragraph [0045]-WANG discloses based on the banding map of a color channel, a per-channel banding spread is computed at operation 706. Based on the banding map, a per-channel banding strength is computed 708. Further in paragraph [0046]-WANG discloses the resulting per-channel banding spread 710 and per-channel banding strength 712, are then combined to an overall banding level measure of the channel at operation 714. Moreover, in paragraph [0047]-WANG discloses the per-channel banding level measures are combined into a single per-frame banding level measure at operation 718. The resulting banding level measure 720 is a scalar banding assessment result for the image or video frame).
Regarding claim 15, WANG in view of XU and in further view of SU explicitly teach the method of claim 1, WANG in view of XU fail to explicitly teach wherein the first dynamic range comprises Standard Dynamic Range (SDR) and the second dynamic range comprises High Dynamic Range (HDR).
However, SU explicitly teaches wherein the first dynamic range comprises Standard Dynamic Range (SDR) and the second dynamic range comprises High Dynamic Range (HDR) (Fig. 1. Paragraph [0033]-SU discloses a residual mask can be generated based on differences between a predicted image predicted from a lower dynamic range image (e.g., an SDR image, an intermediate dynamic range image, etc.) that is mapped from an original higher dynamic range (e.g., VDR, etc.). Further in paragraph [0119]-SU discloses the smoothen operator (410) is applied to the prediction image data (e.g., a predicted high bit depth image predicted from a SDR image, an intermediate high dynamic range image, etc.). Further in paragraph [0148]-SU discloses in block 502, a multi-layer video encoder (e.g., 402 of FIG. 4A) determines flat image areas in a predicted image, the predicted image being predicted from a first image mapped from a second image that has a higher dynamic range than the first image).
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 WANG in view of XU and in further view of SU of having a method, comprising: receiving an input image having a first dynamic range, with the teachings of SU of having wherein the first dynamic range comprises Standard Dynamic Range (SDR) and the second dynamic range comprises High Dynamic Range (HDR).
Wherein having WANG’s method having wherein the first dynamic range comprises Standard Dynamic Range (SDR) and the second dynamic range comprises High Dynamic Range (HDR).
The motivation behind the modification would have been to obtain a method that improves the accuracy and efficiency of banding artifact detection and removal, since both WANG and SU systems and methods for image analysis in the context of banding artifacts. Wherein WANG provides systems and methods for automated detection, assessment and removal of visual banding effect in digital images and video signals that balances accuracy and efficiency, while SU provides systems and methods that provide optimal operational parameters for removing or reducing banding artifacts with relatively small distortion and improve bit depth expansion in the false contouring areas (or stair wise areas) significantly both perceptually and objectively. Please see WANG (US 20210321020 A1), Abstract and Paragraph [0010] and SU (US 20160021391 A1), Abstract and Paragraph [0031, 0084 and 0087].
Regarding claim 18, WANG in view of XU and in further view of SU explicitly teach the method of claim 1, WANG further teaches wherein the input image is included in a frame of content being streamed or displayed (Fig. 1. Paragraph [0019]-WANG discloses a video stream is a sequence of multiple such images. The images of a video stream are often referred to as frames. The techniques for computing banding pixels and banding maps described herein operate on images, either as standalone images or as frames of video. Further in paragraph [0033]-WANG discloses the maximum and minimum luminance levels 406 of the display device may be used to determine the luminance range of the display device and subsequently adjust the significance threshold at operation 408. This information may be received from the display device in an example, or may be retrieved from storage including information regarding the capabilities of various display devices).
Regarding claim 19, WANG explicitly teaches a processor (Fig. 1. Paragraph [0052]-WANG discloses FIG. 9 illustrates an example computing device 900 for performing automated detection and assessment of visual banding effect in digital images and video signals. The computing device 900 may include memory 902, processor 904, and non-volatile storage 906) comprising:
one or more processing units to perform operations (Fig. 1. Paragraph [0052]-WANG discloses the processor 904 may include one or more devices selected from high-performance computing (HPC) systems including high-performance cores, microprocessors, micro-controllers, digital signal processors, microcomputers, central processing units, field programmable gate arrays, programmable logic devices, state machines, logic circuits, analog circuits, digital circuits, or any other devices that manipulate signals (analog or digital) based on computer-executable instructions residing in memory 902. Further in paragraph [0054]-WANG discloses upon execution by the processor 904, the computer-executable instructions of the program instructions 908 may cause the computing device 900 to implement one or more of the algorithms and/or methodologies disclosed herein) comprising:
receiving an input image having a first dynamic range (Fig. 5. Paragraph [0018]-WANG discloses the present disclosure applies to both standard dynamic range (SDR) and high dynamic range (HDR) images and videos. Further in paragraph [0022]-WANG discloses the banding effect may be detected and assessed in HDR images/videos (wherein a High dynamic image/video has a first dynamic range));
Although WANG explicitly teaches processing the input image to generate a band size map that identifies at least one banding artifact in the input image (Fig. 6. Paragraph [0022] -WANG discloses the detection and assessment of the banding effect may be determined from an image or video clip at the pixel level. Further in paragraph [0042]-WANG discloses FIG. 6 illustrates an example method for generating banding maps. The banding map may be created for an image or video frame from the detected banding pixels. This process results in a binary map of the same size as the image or video frame being assessed, where the positions of detected banding pixels are marked as a first value (e.g., 1, true, etc.), and the rest of the map is marked as a second value (e.g., 0, false, etc.). This banding map reveals how much the banding effect is visible at each spatial location in an image or video frame. Further in paragraph [0043]-WANG discloses the detected banding pixels at each image or video frame may be aggregated to several banding assessment measurement, including per-channel and per-frame banding spread, banding strength, and banding level measures. Please also read paragraph [0019 and 0040-0041]);
WANG fails to explicitly teach processing, using a neural network, the input image to generate a band size map.
However, XU explicitly teaches processing, using a neural network, the input image to generate a band size map (Fig. 1. Paragraph [0062]-XU discloses regarding the architecture of the feed-forward neural network 34, given the features extracted from each image block, a the feed-forward neural network 34 is trained, e.g., via the training module 40, to learn a classifier that maps a feature vector to a binary value, e.g., 0 or 1, where 0 represents “no banding” and 1 represents “banding.” (wherein stochastic training and optimization and multiple filters, including a smoothing filter may be used)).
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 WANG of having a processor comprising: one or more processing units to perform operations comprising: receiving an input image having a first dynamic range; processing the input image to generate a band size map that identifies at least one banding artifact in the input image, with the teachings of XU of having processing, using a neural network, the input image to generate a band size map.
Wherein having WANG’s processor having processing, using a neural network, the input image to generate a band size map that identifies at least one banding artifact in the input image;
The motivation behind the modification would have been to obtain a processor that improves the accuracy and efficiency of banding artifact detection and removal, since both WANG and XU systems and methods for image and banding artifact analysis. Wherein WANG provides systems and methods for automated detection, assessment and removal of visual banding effect in digital images and video signals that balances accuracy and efficiency, while XU provides systems and methods that improve the accuracy of banding prediction based on color and gradient features in YCbCr color spaces and use of a feed-forward neural network classifier 34. Please see WANG et al. (US 20210321020 A1), Abstract and Paragraph [0010] and XU et al. (US 20160360202 A1), Abstract and Paragraph [0069].
WANG in view of XU fail to explicitly teach generating, using a blur filter, a de-banded image based on the band size map and the input image, wherein the blur filter removes at least a portion of the at least one banding artifact from the input image; and generating, based on the de-banded image, an output image having a second dynamic range greater than the first dynamic range.
However, SU explicitly teaches generating, using a blur filter, a de-banded image based on the band size map (Fig. 1. Paragraph [0089]-SU discloses in block 302, image areas with a high confidence level to have false contouring are determined in an image (e.g., a predicted high bit depth image, etc.). Image areas computed with the largest possible N in Ω represent high confidence image areas (or collectively a confidence area in short) for flat areas (e.g., having false contouring, etc.). Please also read paragraph [0056-0060 and 0099-0103]) and the input image (Fig. 1. Paragraph [0055]-SU discloses an input image 101 (e.g., a VDR image, a predicted high bit depth image predicted from a low bit depth image, a decoded image that comprises banding artifacts, etc.) to which the decontouring operations are to be performed is denoted as v), wherein the blur filter removes at least a portion of the at least one banding artifact from the input image (Fig. 1. Paragraph [0067]-SU discloses a blur filter can be used at least as a part of the smoothen operator to remove banding artifacts in a high bit depth image (e.g., that is predicted from a low bit depth image that lost original real values in quantization, etc.). A convolution of a 2-D (separable or non-separable) low pass kernel, etc., may be used to generate smooth area over flat areas or stair wise areas);
and generating, based on the de-banded image, an output image having a second dynamic range greater than the first dynamic range (Fig. 1. Paragraph [0119]-SU discloses the smoothen operator (410) is applied to the prediction image data (e.g., intermediate high dynamic range image, etc.) to generate smoothened prediction image data (e.g., a smoothened high bit depth image, etc.). Further in paragraph [0148]-SU discloses in block 502, a multi-layer video encoder (e.g., 402 of FIG. 4A) determines flat image areas in a predicted image, the predicted image being predicted from a first image mapped from a second image that has a higher dynamic range than the first image. Please also read paragraph [0122-0123, 0129, 0133 and 0139-0140]).
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 WANG in view of XU of having a processor comprising: one or more processing units to perform operations comprising: receiving an input image having a first dynamic range; processing the input image to generate a band size map that identifies at least one banding artifact in the input image, with the teachings of SU of having generating, using a blur filter, a de-banded image based on the band size map and the input image, wherein the blur filter removes at least a portion of the at least one banding artifact from the input image; and generating, based on the de-banded image, an output image having a second dynamic range greater than the first dynamic range.
Wherein having WANG’s processor having generating, using a blur filter, a de-banded image based on the band size map and the input image, wherein the blur filter removes at least a portion of the at least one banding artifact from the input image; and generating, based on the de-banded image, an output image having a second dynamic range greater than the first dynamic range.
The motivation behind the modification would have been to obtain a processor that improves the accuracy and efficiency of banding artifact detection and removal, since both WANG and SU systems and methods for image analysis in the context of banding artifacts. Wherein WANG provides systems and methods for automated detection, assessment and removal of visual banding effect in digital images and video signals that balances accuracy and efficiency, while SU provides systems and methods that provide optimal operational parameters for removing or reducing banding artifacts with relatively small distortion and improve bit depth expansion in the false contouring areas (or stair wise areas) significantly both perceptually and objectively. Please see WANG (US 20210321020 A1), Abstract and Paragraph [0010] and SU (US 20160021391 A1), Abstract and Paragraph [0031, 0084 and 0087].
Regarding claim 20, WANG explicitly teaches a system (Fig. 1. Paragraph [0052]-WANG discloses FIG. 9 illustrates an example computing device 900 for performing automated detection and assessment of visual banding effect in digital images and video signals. The computing device 900 may include memory 902, processor 904, and non-volatile storage 906) comprising:
one or more processors to perform operations (Fig. 1. Paragraph [0052]-WANG discloses the processor 904 may include one or more devices selected from high-performance computing (HPC) systems including high-performance cores, microprocessors, micro-controllers, digital signal processors, microcomputers, central processing units, field programmable gate arrays, programmable logic devices, state machines, logic circuits, analog circuits, digital circuits, or any other devices that manipulate signals (analog or digital) based on computer-executable instructions residing in memory 902. Further in paragraph [0054]-WANG discloses upon execution by the processor 904, the computer-executable instructions of the program instructions 908 may cause the computing device 900 to implement one or more of the algorithms and/or methodologies disclosed herein) comprising:
receiving an input image having a first dynamic range (Fig. 5. Paragraph [0018]-WANG discloses the present disclosure applies to both standard dynamic range (SDR) and high dynamic range (HDR) images and videos. In paragraph [0022]-WANG discloses the banding effect may be detected and assessed in HDR images/videos (wherein High dynamic range images/videos have a first dynamic range));
Although WANG explicitly teaches processing the input image to generate a band size map that identifies at least one banding artifact in the input image (Fig. 6. Paragraph [0022] -WANG discloses the detection and assessment of the banding effect may be determined from an image or video clip at the pixel level. Further in paragraph [0042]-WANG discloses FIG. 6 illustrates an example method for generating banding maps. The banding map may be created for an image or video frame from the detected banding pixels. This process results in a binary map of the same size as the image or video frame being assessed, where the positions of detected banding pixels are marked as a first value (e.g., 1, true, etc.), and the rest of the map is marked as a second value (e.g., 0, false, etc.). This banding map reveals how much the banding effect is visible at each spatial location in an image or video frame. Further in paragraph [0043]-WANG discloses the detected banding pixels at each image or video frame may be aggregated to several banding assessment measurement, including per-channel and per-frame banding spread, banding strength, and banding level measures. Please also read paragraph [0019 and 0040-0041]);
WANG fails to explicitly teach processing, using a banding detector neural network, the input image to generate a band size map.
However, XU explicitly teaches processing, using a banding detector neural network, the input image to generate a band size map (Fig. 1. Paragraph [0062]-XU discloses regarding the architecture of the feed-forward neural network 34, given the features extracted from each image block, a the feed-forward neural network 34 is trained, e.g., via the training module 40, to learn a classifier that maps a feature vector to a binary value, e.g., 0 or 1, where 0 represents “no banding” and 1 represents “banding.” (wherein stochastic training and optimization and multiple filters, including a smoothing filter may be used)).
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 WANG of having a system comprising: one or more processors to perform operations comprising: receiving an input image having a first dynamic range; processing the input image to generate a band size map that identifies at least one banding artifact in the input image, with the teachings of XU of having processing, using a banding detector neural network, the input image to generate a band size map.
Wherein having WANG’s system having processing, using a banding detector neural network, the input image to generate a band size map to generate a band size map that identifies at least one banding artifact in the input image
The motivation behind the modification would have been to obtain a system that improves the accuracy and efficiency of banding artifact detection and removal, since both WANG and XU systems and methods for image and banding artifact analysis. Wherein WANG provides systems and methods for automated detection, assessment and removal of visual banding effect in digital images and video signals that balances accuracy and efficiency, while XU provides systems and methods that improve the accuracy of banding prediction based on color and gradient features in YCbCr color spaces and use of a feed-forward neural network classifier 34. Please see WANG et al. (US 20210321020 A1), Abstract and Paragraph [0010] and XU et al. (US 20160360202 A1), Abstract and Paragraph [0069].
WANG in view of XU fails to explicitly teach generating, using a blur filter, a de-banded image based on the band size map and the input image, wherein the blur filter removes at least a portion of the at least one banding artifact from the input image; and generating, based on the de-banded image, an output image having a second dynamic range greater than the first dynamic range.
However, SU explicitly teaches generating, using a blur filter, a de-banded image based on the band size map (Fig. 1. Paragraph [0089]-SU discloses in block 302, image areas with a high confidence level to have false contouring are determined in an image (e.g., a predicted high bit depth image, etc.). Image areas computed with the largest possible N in Ω represent high confidence image areas (or collectively a confidence area in short) for flat areas (e.g., having false contouring, etc.). Please also read paragraph [0056-0060 and 0099-0103]) and the input image (Fig. 1. Paragraph [0055]-SU discloses an input image 101 (e.g., a VDR image, a predicted high bit depth image predicted from a low bit depth image, a decoded image that comprises banding artifacts, etc.) to which the decontouring operations are to be performed is denoted as v), wherein the blur filter removes at least a portion of the at least one banding artifact from the input image (Fig. 1. Paragraph [0067]-SU discloses a blur filter can be used at least as a part of the smoothen operator to remove banding artifacts in a high bit depth image (e.g., that is predicted from a low bit depth image that lost original real values in quantization, etc.). A convolution of a 2-D (separable or non-separable) low pass kernel, etc., may be used to generate smooth area over flat areas or stair wise areas); and
generating, based on the de-banded image, an output image having a second dynamic range greater than the first dynamic range (Fig. 1. Paragraph [0119]-SU discloses the smoothen operator (410) is applied to the prediction image data (e.g., intermediate high dynamic range image, etc.) to generate smoothened prediction image data (e.g., a smoothened high bit depth image, etc.). Further in paragraph [0148]-SU discloses in block 502, a multi-layer video encoder (e.g., 402 of FIG. 4A) determines flat image areas in a predicted image, the predicted image being predicted from a first image mapped from a second image that has a higher dynamic range than the first image. Please also read paragraph [0122-0123, 0129, 0133 and 0139-0140]).
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 WANG in view of XU of having a system comprising: one or more processors to perform operations comprising: receiving an input image having a first dynamic range; processing the input image to generate a band size map that identifies at least one banding artifact in the input image, with the teachings of SU of having generating, using a blur filter, a de-banded image based on the band size map and the input image, wherein the blur filter removes at least a portion of the at least one banding artifact from the input image; and generating, based on the de-banded image, an output image having a second dynamic range greater than the first dynamic range.
Wherein having WANG’s system having generating, using a blur filter, a de-banded image based on the band size map and the input image, wherein the blur filter removes at least a portion of the at least one banding artifact from the input image; and generating, based on the de-banded image, an output image having a second dynamic range greater than the first dynamic range.
The motivation behind the modification would have been to obtain a system that improves the accuracy and efficiency of banding artifact detection and removal, since both WANG and SU systems and methods for image analysis in the context of banding artifacts. Wherein WANG provides systems and methods for automated detection, assessment and removal of visual banding effect in digital images and video signals that balances accuracy and efficiency, while SU provides systems and methods that provide optimal operational parameters for removing or reducing banding artifacts with relatively small distortion and improve bit depth expansion in the false contouring areas (or stair wise areas) significantly both perceptually and objectively. Please see WANG (US 20210321020 A1), Abstract and Paragraph [0010] and SU (US 20160021391 A1), Abstract and Paragraph [0031, 0084 and 0087].
Regarding claim 21, WANG in view of XU and in further view of SU explicitly teaches the system of claim 19, WANG further teaches wherein the system comprises at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system implementing one or more large language models (LLMs); a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (Fig. 9. Paragraph [0052]-WANG discloses FIG. 9 illustrates an example computing device 900 for performing automated detection and assessment of visual banding effect in digital images and video signals. The non-volatile storage 906 may include one or more persistent data storage devices such as cloud storage. Please also read paragraph [0042]);
Claims 4-5 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over WANG et al. (US 20210321020 A1), hereinafter referenced as WANG in view of XU et al. (US 20160360202 A1), hereinafter referenced as XU and in further view of SU et al. (US 20160021391 A1), hereinafter referenced as SU and in further view of WANG et. al. (US 20230131228 A1), hereinafter referenced as WANG (2023).
Regarding claim 4, WANG in view of XU and in further view of SU explicitly teach the method of claim 3, although WANG further teaches wherein the at least one channel in the plurality of channels is associated with a plurality of binary values and at least one binary value in the plurality of binary values corresponds to a respective pixel of the input image (Fig. 6. Paragraph [0024]-WANG discloses at operation 202, the image or each video frame are decomposed into luminance or color channels. In paragraph [0026]-WANG discloses at operation 206, banding pixels 208 are determined. Banding pixels 208 may be determined as being any pixels in the image or video frame that 1) have significant signal activity at their locations as defined herein; and 2) where at least a minimum threshold percentage of their neighboring pixels have non-significant signal activity. In paragraph [0041]-WANG discloses the detected banding pixels 500 are measured for their signal activity at operation 502 (wherein if the signal activity is higher, than the pixel location is removed from the collection of detected banding pixels 508). In paragraph [0042]-WANG discloses this process results in a binary map of the same size as the image or video frame being assessed, where the positions of detected banding pixels are marked as a first value (e.g., 1, true, etc.), and the rest of the map is marked as a second value (e.g., 0, false, etc.) (wherein signal activity is normalized based on the significance threshold at operation 606). Please also read paragraph [0026, 0040 and 0043-0046]).
WANG in view of XU and in further view of SU fail to explicitly teach and at least one binary value in the plurality of binary