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 .
Response to Arguments
Applicant's arguments filed 11/12/2025 have been fully considered but they are not persuasive.
Applicant argues that Nalci does not explicitly teach the transform skip module is a connection in the compression framework that directly passes an input signal related the video unit to at least one of: an intermediate stage or a final stage of the compression framework.
In response, the examiner respectfully disagrees. Nalci teaches encoder 200 may operate as a hybrid neural network encoder. A hybrid codec may include at least one first coding tool having a corresponding alternative coding tool. The corresponding tool alternatives may be first encoding tool and a neural network tool, where the first encoding tool does not include a neural network and the neural network coding tool either does include a neural network or may be designed to work with another neural network coding tool. The first encoder may act as a host to the neural network alternative tool. For example, an optional neural network encoder 292 may encode pixel blocks, for example using a convolutional neural network such as depicted in FIG. 8. An optional selector 290 may select between the host codec's pixel block encoder 210 or the alternative's neural network encoder 292. Controller 270 may select between these encoders, for example, on a per-pixel-block basis or per-superblock of pixel blocks. Controller 270 may include an indication of the encoder selections in the side information included in the encoded output of encoder system 200. In an alternate hybrid aspect, an encoder selector may be positioned after subtractor 212 such that the neural network encoder may encode residual blocks instead of source pixel blocks, as depicted in FIG. 2. [0052] – [0065], Fig. 2.
As shown in Fig. 2, the NN encoder 292 is a connection in the compression framework that directly passes an input signal related to the video unit from the selector 290 to the entropy coder & syntax unit (a final stage).
FIGS. 15A and 15B depict an example encoder 1500 and decoder 1550, respectively, with hybrid host and neural network coding tools. In FIG. 15A, for a portion of image data input to encoder 1500, such as source frame, encoder decision unit 1502 controls switch 1504 for selecting between a first codec's encoding tools, such as host codec's tools 1506, and an alternate codec's coding tools, such as neural network-based process 1510. Switch 1508 may select a bitstream output from the selected encoding tool for inclusion in a hybrid bitstream by bitstream packer 1512. Side information indicating the encoder selected by the encoder decision unity for the portion of image data may be included also be included in the final bitstream, for example, by the bitstream packer 1512.
Decoder 1550 may operate as a complement to encoder 1500. An encoded hybrid bitstream may be parsed by a decoder control 1552 to identify decoder selection for a portion of image data, such as by identifying a decoder selection flag explicitly encoded in the hybrid bitstream or by inferring the decoder decision from other information in the hybrid bitstream. Switch 1554 may select between a first decoder, such as host codec decoder 1556, and an alternate decoder, such as neural network-based decoder 1558 based on the decoder decision identified by decoder control 1552, and a portion of the hybrid bitstream corresponding to the image portion may be provided to the selected decoder. Switch 1560 may select between the outputs of the first or alternate decoders for inclusion in a reconstruction of the image portion (for example a complete image frame).
In one embodiment, a host codec and the NN-based compression scheme can be combined as in FIG. 15A/B. At the encoder side, an entire source image or frame can be compressed either with the NN-based compression process in an end-to-end manner using the NN architecture shown in FIG. 9, or alternatively the host encoder can decide to compress the entire image using its existing coding tools bypassing the NN-based compression scheme. The process of which compression scheme will be used can be made by the host codec's encoder based on, for example, a rate-distortion (RD) analysis/rules. This is illustrated with the switch after the Encoder Decision block in FIG. 15A/B. If NN compression is not used, the encoder falls back to using its host codec coding tools for the particular frame, whereas if the RD cost is lower with the NN process, then NN compression is used by the host codec. This decision can alter the parsing and decoding process of the host-codec's decoder. [0117] – [0136], Fig. 15A-15B and Fig. 16A-16B.
As shown in Fig. 15A, the NN-based compression process is a connection in the compression framework that directly passes an input signal related to the video unit from encoder decision unit that controls the switch 1504 to switch 1508 (an intermediate stage).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nalci et al. (US 2023/0096567 A1).
Consider claim 1, Nalci teaches a method of video processing, comprising: applying, during a conversion between a video unit of a video and a bitstream of the video unit, a compression process to the video unit based on a compression framework ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B), wherein the compression framework comprises a transform skip module ([0079], [0119], [0126] – [0129], [0149], [0154] – [0156], [0170] – [0178]); wherein the transform skip module is a connection in the compression framework that directly passes an input signal related the video unit to at least one of: an intermediate stage or a final stage of the compression framework (the NN encoder/NN-based compression process is a connection in the compression framework that directly passes an input signal related the video unit to the intermediate stage or final stage [0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B); and performing the conversion based on the compressed video unit ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B),
Consider claim 3, Nalci teaches the transform skip module is a subset of the compression framework that comprises less feature extraction units (the NN encoder/NN-based compression process comprise less feature extraction units than the host codec. The host codec comprises pixel block encoder, pixel block decoder, frame buffer, filter, reference picture buffer, and predictor. These components comprise several sub-components. [0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B) and larger scaling ratios than those of other modules of the compression framework (the primary transform module g.sub.a at the encoder end uses a serial cascade of 2D spatial convolution operations with spatial downsampling followed by a normalization operation called Generalized Divisive Normalization (GDN), which normalizes the features obtained by the convolutional layers. As each layer downsamples the image by a factor of 2, the output of the primary transform has fewer coefficients than the number of pixels in the input image (i.e., compressed version of input). These output coefficients y can be quantized (box labeled “Q” in FIG. 8) into 9, which may then can be entropy encoded into a bitstream (checkerboard boxes in FIG. 8) for example using arithmetic encoding (AE) methods. This stage resembles the transform and quantization stage in conventional video codecs; however, it can implicitly perform other operations as well such as intra prediction without specifying this need. [0099] and [0102]).
Consider claim 4, Nalci teaches the transform skip module is applied in a transform analysis stage of the compression process, wherein an input of the transform analysis state is a visual signal or features of the visual signal (input video and source frame. [0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B), wherein a transform analysis branch in the transform analysis stage comprises T.sub.1 feature extraction units ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. The transform analysis branch is the host codec that includes pixel block encoder, pixel block decoder, and, frame buffer, filter, reference picture buffer, and predictor in Fig. 2. Host codec in Fig. 15A. Subset of the Host Codec in Fig. 16A), wherein a transform skip analysis branch that includes the transform skip module comprises T.sub.2 feature extraction units ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. NN encoder in Fig. 2. NN-based compression process in Fig. 15A and Fig. 16A), and wherein T.sub.2 is not larger than T.sub.1, T.sub.1 and T.sub.2 are integer numbers ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. NN encoder in Fig. 2. NN-based compression process in Fig. 15A and Fig. 16A).
Consider claim 5, Nalci teaches a transform skip analysis branch that includes the transform skip module substitutes an entire main transform branch of the compression framework ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. an NN-based compression process can be integrated into an existing image/video coding system (such as a conventional coding system) to replace or bypass parts (or certain coding tools) of existing “host” codec. [0118]).
Consider claim 6, Nalci teaches a portion of feature extraction units in a main transform branch is replaced by a set of feature extraction units related to transform skip ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. an NN-based compression process can be integrated into an existing image/video coding system (such as a conventional coding system) to replace or bypass parts (or certain coding tools) of existing “host” codec. [0118]), and wherein a feature extraction unit related to transform skip comprises a larger scaling ratio than that of the feature extraction unit in the main transform branch of the compression framework (the primary transform module g.sub.a at the encoder end uses a serial cascade of 2D spatial convolution operations with spatial downsampling followed by a normalization operation called Generalized Divisive Normalization (GDN), which normalizes the features obtained by the convolutional layers. As each layer downsamples the image by a factor of 2, the output of the primary transform has fewer coefficients than the number of pixels in the input image (i.e., compressed version of input). These output coefficients y can be quantized (box labeled “Q” in FIG. 8) into 9, which may then can be entropy encoded into a bitstream (checkerboard boxes in FIG. 8) for example using arithmetic encoding (AE) methods. This stage resembles the transform and quantization stage in conventional video codecs; however, it can implicitly perform other operations as well such as intra prediction without specifying this need. [0099] and [0102]).
Consider claim 7, Nalci teaches a transform skip analysis branch that includes the transform skip module is parallel to a main transform branch of the compression framework ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. NN encoder and host codec are parallel in Fig. 2. NN-based compression process and host codec are parallel in Fig. 15A and Fig. 16A).
Consider claim 8, Nalci teaches the transform skip module is applied in a hyperprior analysis stage of the compression process, wherein an input of the hyperprior analysis stage is latent code y ([0086], [0099], [0108], [0196] – [0197]), wherein a hyperprior analysis branch in the hyperprior analysis stage comprises H.sub.1 feature extraction units ([0052] – [0065], [0086], [0099] – [0101], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. The hyperprior analysis branch is the host codec that includes pixel block encoder, pixel block decoder, and, frame buffer, filter, reference picture buffer, and predictor in Fig. 2. Host codec in Fig. 15A. Subset of the Host Codec in Fig. 16A), wherein a transform skip hyperprior analysis branch that includes the transform skip module comprises H.sub.2 feature extraction units ([0052] – [0065], [0086], [0099] – [0101], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. The transform skip hyperprior analysis branch is the NN encoder that includes hyperprior encoder in Fig. 2 and Fig. 8. NN-based compression process in Fig. 15A and Fig. 16A), and wherein H.sub.2 is not larger than H.sub.1, H.sub.1 and H.sub.2 are integer numbers ([0052] – [0065], [0086], [0099] – [0101], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. The hyperprior analysis branch is the host codec that includes pixel block encoder, pixel block decoder, and, frame buffer, filter, reference picture buffer, and predictor in Fig. 2. Host codec in Fig. 15A. Subset of the Host Codec in Fig. 16A. The transform skip hyperprior analysis branch is the NN encoder that includes hyperprior encoder in Fig. 2 and Fig. 8. NN-based compression process in Fig. 15A and Fig. 16A).
Consider claim 9, Nalci teaches the transform skip hyperprior analysis branch that includes the transform skip module substitutes an entire hyperprior analysis branch of the compression framework ([0052] – [0065], [0099] – [0101], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. An NN-based compression process can be integrated into an existing image/video coding system (such as a conventional coding system) to replace or bypass parts (or certain coding tools) of existing “host” codec. [0118]).
Consider claim 10, Nalci teaches a portion of feature extraction units in a hyperprior analysis branch is replaced by a set of feature extraction units related to transform skip ([0052] – [0065], [0099] – [0101], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. an NN-based compression process can be integrated into an existing image/video coding system (such as a conventional coding system) to replace or bypass parts (or certain coding tools) of existing “host” codec. [0118]), and wherein a feature extraction unit related to transform skip comprises a larger scaling ratio than that of the feature extraction unit in the hyperprior analysis branch of the compression framework (the primary transform module g.sub.a at the encoder end uses a serial cascade of 2D spatial convolution operations with spatial downsampling followed by a normalization operation called Generalized Divisive Normalization (GDN), which normalizes the features obtained by the convolutional layers. As each layer downsamples the image by a factor of 2, the output of the primary transform has fewer coefficients than the number of pixels in the input image (i.e., compressed version of input). These output coefficients y can be quantized (box labeled “Q” in FIG. 8) into 9, which may then can be entropy encoded into a bitstream (checkerboard boxes in FIG. 8) for example using arithmetic encoding (AE) methods. This stage resembles the transform and quantization stage in conventional video codecs; however, it can implicitly perform other operations as well such as intra prediction without specifying this need. [0099] and [0102]).
Consider claim 11, Nalci teaches a transform skip hyperprior analysis branch that includes the transform skip module is parallel to a hyperprior analysis branch of the compression framework ([0052] – [0065], [0099] – [0101], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B. NN encoder and host codec are parallel in Fig. 2. NN-based compression process and host codec are parallel in Fig. 15A and Fig. 16A).
Consider claim 12, Nalci teaches the transform skip module is applied in a hyperprior synthesis stage of the compression process, wherein an input of the hyperprior synthesis stage is a latent code of a hyper-prior {circumflex over (z)} ([0100] and [0114]), wherein a hyperprior synthesis branch in the hyperprior synthesis stage comprises H.sub.3 hyperprior reconstruction units ([0052] – [0065], [0086], [0099] – [0101], [0117] – [0136], Fig. 3, Fig. 15A, 15B, 16A, 16B. The hyperprior reconstruction branch is the host codec that includes pixel block decoder, pred. blk., predictor, reference picture buffer in Fig. 3. Host codec in Fig. 15A. Subset of the Host Codec in Fig. 16A), wherein a transform skip hyperprior reconstruction branch that includes the transform skip module comprises H.sub.4 hyperprior reconstruction units ([0052] – [0065], [0086], [0099] – [0101], [0117] – [0136], Fig. 3, Fig. 15A, 15B, 16A, 16B. The transform skip hyperprior reconstruction branch NN decoder in Fig. 3. NN-based decompression process in Fig. 15B. NN-based decompression process in Fig. 16B), and wherein H.sub.4 is not larger than H.sub.3, H.sub.3 and H.sub.4 are integer numbers ([0052] – [0065], [0086], [0099] – [0101], [0117] – [0136], Fig. 3, Fig. 15A, 15B, 16A, 16B. The hyperprior reconstruction branch is the host codec that includes pixel block decoder, pred. blk., predictor, reference picture buffer in Fig. 3. Host codec in Fig. 15A. Subset of the Host Codec in Fig. 16A. The transform skip hyperprior reconstruction branch NN decoder in Fig. 3. NN-based decompression process in Fig. 15B. NN-based decompression process in Fig. 16B).
Consider claim 13, Nalci teaches a transform skip hyperprior reconstruction branch that includes the transform skip module substitutes an entire hyperprior synthesis branch of the compression framework ([0052] – [0065], [0086], [0099] – [0101], [0117] – [0136], Fig. 3, Fig. 15A, 15B, 16A, 16B. The hyperprior reconstruction branch is the host codec that includes pixel block decoder, pred. blk., predictor, reference picture buffer in Fig. 3. Host codec in Fig. 15A. Subset of the Host Codec in Fig. 16A. The transform skip hyperprior reconstruction branch NN decoder in Fig. 3. NN-based decompression process in Fig. 15B. NN-based decompression process in Fig. 16B).
Consider claim 14, Nalci teaches the transform skip module is applied in a transform synthesis stage of the compression process, wherein an input of the transform synthesis state is a latent code from an entropy decoder ([0052] – [0065], [0075], [0086], [0097] – [0105], [0114], [0117] – [0136], Fig. 3, Fig. 15A, 15B, 16A, 16B), wherein a transform synthesis branch in the transform synthesis stage comprises T.sub.3 feature reconstruction units ([0052] – [0065], [0075], [0086], [0097] – [0105], [0114], [0117] – [0136], Fig. 3, Fig. 15A, 15B, 16A, 16B. The transform synthesis branch is the host codec that includes pixel block decoder, pred. blk., predictor, reference picture buffer in Fig. 3. Host codec in Fig. 15A. Subset of the Host Codec in Fig. 16A. The transform skip synthesis branch NN decoder in Fig. 3. NN-based decompression process in Fig. 15B. NN-based decompression process in Fig. 16B), wherein a transform skip synthesis branch that includes the transform skip module comprises T.sub.4 feature reconstruction units ([0052] – [0065], [0075], [0086], [0097] – [0105], [0114], [0117] – [0136], Fig. 3, Fig. 15A, 15B, 16A, 16B. The transform synthesis branch is the host codec that includes pixel block decoder, pred. blk., predictor, reference picture buffer in Fig. 3. Host codec in Fig. 15A. Subset of the Host Codec in Fig. 16A. The transform skip synthesis branch NN decoder in Fig. 3. NN-based decompression process in Fig. 15B. NN-based decompression process in Fig. 16B), and wherein T.sub.4 is not larger than T.sub.3, T.sub.3 and T.sub.4 are integer numbers ([0052] – [0065], [0075], [0086], [0097] – [0105], [0114], [0117] – [0136], Fig. 3, Fig. 15A, 15B, 16A, 16B. The transform synthesis branch is the host codec that includes pixel block decoder, pred. blk., predictor, reference picture buffer in Fig. 3. Host codec in Fig. 15A. Subset of the Host Codec in Fig. 16A. The transform skip synthesis branch NN decoder in Fig. 3. NN-based decompression process in Fig. 15B. NN-based decompression process in Fig. 16B).
Consider claim 15, Nalci teaches a scaling factor controls changing of a dimension of features, wherein the dimension is a spatial resolution and a channel-wise dimension (the primary transform module g.sub.a at the encoder end uses a serial cascade of 2D spatial convolution operations with spatial downsampling followed by a normalization operation called Generalized Divisive Normalization (GDN), which normalizes the features obtained by the convolutional layers. As each layer downsamples the image by a factor of 2, the output of the primary transform has fewer coefficients than the number of pixels in the input image (i.e., compressed version of input). These output coefficients y can be quantized (box labeled “Q” in FIG. 8) into 9, which may then can be entropy encoded into a bitstream (checkerboard boxes in FIG. 8) for example using arithmetic encoding (AE) methods. This stage resembles the transform and quantization stage in conventional video codecs; however, it can implicitly perform other operations as well such as intra prediction without specifying this need. [0099] and [0102]).
Consider claim 16, Nalci teaches the compression framework comprises a main branch and a transform skip branch that includes the transform skip module (In other aspects for hybrid encoding, any of the processing tools of encoder system 200 may optionally include alternate versions, such as a host codec version and a neural network-based version. Example coding tools with alternate versions may include the partitioner and pre-filter 205, pixel block coder 210, pixel block decoder 220, frame buffer 230, in loop filter system 240, reference picture buffer 250, predictor 260, and an entropy coder and syntax unit 280. Controller 270 may select between host tool and neural network tool for each hybrid tool, and an indication of the selections between these tools may be included as operational parameter side information in the coded output data. A cascade 1400 of such hybrid coding tools is depicted in FIG. 14A, where hybrid encoding tools 1410.1-1410.N each includes a switch 1416.x controlled by controller 1420 for selecting between host tools 1412.x and neural network tools 1414.x and may produce side information of the host/neural-network selections. [0066]. Fig. 14A-Fig. 14B), and wherein a first output of the main branch and a second output of the transform skip branch are combined and fed to a next stage in the compression framework (In other aspects for hybrid encoding, any of the processing tools of encoder system 200 may optionally include alternate versions, such as a host codec version and a neural network-based version. Example coding tools with alternate versions may include the partitioner and pre-filter 205, pixel block coder 210, pixel block decoder 220, frame buffer 230, in loop filter system 240, reference picture buffer 250, predictor 260, and an entropy coder and syntax unit 280. Controller 270 may select between host tool and neural network tool for each hybrid tool, and an indication of the selections between these tools may be included as operational parameter side information in the coded output data. A cascade 1400 of such hybrid coding tools is depicted in FIG. 14A, where hybrid encoding tools 1410.1-1410.N each includes a switch 1416.x controlled by controller 1420 for selecting between host tools 1412.x and neural network tools 1414.x and may produce side information of the host/neural-network selections. [0066]. Fig. 14A-Fig. 14B. Fig. 14A shows that the hybrid coding tools is able to select different encoding tools at different stage. For instance, at 1410.1, the Host Tool is selected and its output will be the input to 1410.2. At 1410.2, NN tool is selected, its output will be combined with the output from 1410.1 as an input to the next stage.
Alternatively, FIG. 4A depicts an example hybrid encoding method 400 with encoder selection. Method 400 may be implemented, for example, with the encoders of FIG. 2, 15A or 16A. For each portion of source video, an encoder is selected (box 404) between a first encoder and an alternative encoder, such as between a host encoder and a neural network-based encoder. When the host encoder is selected for a portion of source video, that portion is encoded by the host encoder (406) to produce a host encoded bitstream, and when the neural network-based encoder is selected for the portion of source video, the portion is encoded by the neural network-based encoder (410) to produce a neural network-based bitstream. Portions encoded in both the host bitstream and neural network bitstream may be combined into a combined bitstream (412), for example by syntax and entropy coder 280 of FIG. 2 or another type of bitstream packing unit. [0079] and Fig. 4A).
Consider claim 17, Nalci teaches the conversion includes encoding the video unit into the bitstream, or wherein the conversion includes decoding the video unit from the bitstream ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B).
Consider claim 18, Nalci teaches an apparatus for video processing comprising a processor ([0202] – [0204]) and a non-transitory memory with instructions thereon ([0202] – [0204]), wherein the instructions upon execution by the processor, cause the processor to perform the method recited in claim 1 (see rejection for claim 1).
Consider claim 19, Nalci teaches a non-transitory computer-readable storage medium storing instructions that cause a processor ([0202] – [0204]) to perform the method recited in claim 1 (see rejection of claim 1).
Consider claim 20, Nalci teaches a method for storing a bitstream ([0049], [0202] – [0204]) of a video which is generated by a method performed by an apparatus for video processing, wherein the method comprises: applying a compression process to a video unit of the video based on a compression framework, wherein the compression framework comprises a transform skip module ([0079], [0119], [0126] – [0129], [0149], [0154] – [0156], [0170] – [0178]); wherein the transform skip module is a connection in the compression framework that directly passes an input signal related the video unit to at least one of: an intermediate stage or a final stage of the compression framework (the NN encoder/NN-based compression process is a connection in the compression framework that directly passes an input signal related the video unit to the intermediate stage or final stage [0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B); and generating a bitstream of the video based on the compressed video unit ([0052] – [0065], [0117] – [0136], Fig. 2, Fig. 15A, 15B, 16A, 16B); and storing the bitstream in a non-transitory computer-readable recording medium ([0049], [0202] – [0204]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAT CHI CHIO whose telephone number is (571)272-9563. The examiner can normally be reached Monday-Thursday 10am-5pm.
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/TAT C CHIO/Primary Examiner, Art Unit 2486
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