WO2021068175A1 - Procédé et appareil de compression de séquence vidéo - Google Patents

Procédé et appareil de compression de séquence vidéo Download PDF

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Publication number
WO2021068175A1
WO2021068175A1 PCT/CN2019/110470 CN2019110470W WO2021068175A1 WO 2021068175 A1 WO2021068175 A1 WO 2021068175A1 CN 2019110470 W CN2019110470 W CN 2019110470W WO 2021068175 A1 WO2021068175 A1 WO 2021068175A1
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ics
frame
frames
patch
channel
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PCT/CN2019/110470
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English (en)
Inventor
David Jones Brady
Xuefei YAN
Weiping Zhang
Changzhi YU
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Suzhou Aqueti Technology Co., Ltd.
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Priority to CN201980066142.9A priority Critical patent/CN113196779B/zh
Priority to PCT/CN2019/110470 priority patent/WO2021068175A1/fr
Publication of WO2021068175A1 publication Critical patent/WO2021068175A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/59—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/593—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques

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  • the present invention relates to method and apparatus of video clip compressing, particularly to a method for low power video clip compression.
  • the step for sub-QR-ICS frames corresponding to each ICS-channel, doing patch-matching subtraction in the QR-ICS frames relative to the quantized first ICS-frame comprising: for each sub-QR-ICS frame corresponding to an ICS-channel, doing patch-matching-based motion prediction for patches of the sub-QR-ICS frame relative to the sub-quantized first ICS-frame corresponding to the same ICS-channel, wherein the sub-QR-ICS-frame is divided into patches for patch-searching in the sub-quantized first ICS frames, there is no gap, nor overlap between the patches in the sub-QR-ICS-frame; for each sub-QR-ICS frame, determining a patch-subtracted ICS-frame by subtracting one or more matched patches from the sub-QR-ICS frame.
  • the step for each sub-QR-ICS frame corresponding to an ICS-channel, doing patch-matching-based motion prediction for patches of the sub-QR-ICS frame relative to the sub-quantized first ICS-frame corresponding to the same ICS-channel comprising: defining a sub-QR-ICS frame corresponding to an ICS-channel as a matching frame and a sub-quantized first ICS-frame corresponding to the same ICS-channel as a searching frame, and doing hierarchical patch searching in the searching frame, wherein the matching frame is divided into relative patches and the hierarchical patch searching comprising: for each relative patch in the matching frame, doing patch searching in the searching frame with a stride size, wherein the stride size is a defined integer not smaller than 1; during the patch searching in the searching frame, calculating a square difference between each patch in the searching frame and the relative patch in the matching frame; if the lowest square difference is smaller than a defined threshold, determining a target patch in the searching frame with lowest square difference as a reference
  • the numbers of patch-subtracted ICS-frames in each stack are equal to each other.
  • the step for patch-subtracted ICS-frames in each stack corresponding to each ICS-channel, determining shared data, and for each patch-subtracted ICS-frame, determining a stack-residual frame based on the shared data comprising: for each stack corresponding to an ICS-channel, determining shared data by convoluting the patch-subtracted ICS-frames in each stack with a first kernel; for each stack corresponding to the ICS-channel, determining quantized shared data for each stack by quantizing values in shared data to integers of pre-defined bit depth; for each stack corresponding to the ICS-channel, rescaling the quantized shared data to RQ-shared data; for each stack corresponding to the ICS-channel, reshaping the RQ-shared data to RRQ-shared data by performing deconvolution with a second kernel; for each patch-subtracted ICS-frame in each stack corresponding to the ICS-channel, determining a stack-residual frame by subtracting
  • the step training parameters of the initial temporal module via multi-graph-combined-loss training comprising: determining four computation graphs, wherein the four graphs are procedures using the initial temporal module, wherein the first computation graph G1 represents a procedure keeping both first and second quantization points, the second computation graph G2 represents a procedure keeping first quantization point, the third computation graph G3 represents a procedure keeping second quantization point, and the fourth computation graph G4 represents a procedure keeping none quantization point; wherein the first quantization point represents quantizing output data of the fifth kernel, and the second quantization point represents quantizing output data of the seventh kernel; determining three optimizers in sequential manner during iterative training; wherein the first optimizer is configured to train parameters before the first quantization point to minimize a first total loss which includes DA_E of the first quantization point from G1, DA_E of second quantization point from G3, and reconstruction loss from G4; wherein the second optimizer is configured to train parameters between first and second quantization points to minimize a second total loss which includes
  • the first graph G1 comprising: determining data T2 by inputting data T1 into a first convolution layer with parameters Para (bQ1) , wherein data T1 corresponds to sample patch-subtracted ICS-frames for all stacks corresponding to each channel; determining data T2_Q by quantizing data T2 at the first quantization point; determining data T3 based on data T2_Q, wherein parameters to be trained in the process from data T2_Q to T3 include a first deconvolution layer and a second convolution layer with parameters Para (aQ1, bQ2) , wherein the process further includes a rescaling operation before the first deconvolution layer and a subtracting operation after the second convolution layer; determining data T3_Q by quantizing data T3 at the second quantization point, wherein data T3_Q corresponds to quantized-compressed stack-residual frames; determining data T4 based on data T3_Q, wherein parameters to be trained in the process from data T3_Q
  • the method further comprising: determining the first kernel by integerizing parameters in the fifth intermediate kernel, and determining the second kernel by integerizing parameters in the sixth intermediate kernel, and determining the third kernel by integerizing parameters in the seventh intermediate kernel; determining the fourth kernel by fine-tuning parameters in the eighth intermediate kernel; determining the decompressing kernel and the QINN by fine-tuning parameters in the intermediate decompressing kernel and the intermediate QINN.
  • Another aspect of the present disclosure is directed to an apparatus for compressing a video clip including a reading-out unit, wherein the reading-out unit is configured to read out a plurality of groups of raw pixel values from a camera head; a processor, wherein the processor is configured to perform compression to multi-frames of the video clip, wherein the compression comprising: doing intra-frame compression by compressing each group of raw pixel values into an intra-frame-compressive-sampling (ICS) frame with a compressing kernel, wherein ICS frames include a first ICS-frame and a number of remaining ICS frames (R-ICS frames) after the first ICS-frame in time, and wherein the compressing kernel has Ncomp ICS-channels and Ncomp is an integer not smaller than 1; quantizing the first ICS-frame, and quantizing R-ICS frames into QR-ICS frames, wherein the quantized first ICS-frame comprises Ncomp sub-quantized first ICS-frames and each sub-quantized first ICS-frame correspond to an ICS-channel of the quantized
  • FIG. 2 illustrates an intra-frame compressing method according to some embodiments of the present disclosure
  • FIG. 3 shows a convolution process as described in 204 according to some embodiments of the present disclosure
  • FIG. 4 illustrates an example of intra-frame compression process with frame strategy one as described in 204 according to some embodiments of the present disclosure
  • FIG. 6 is an integer array of the shape [256, 480, 4] after compression of the input pixel values with the compressing kernel;
  • FIG. 7 illustrates a shared data determining method after the intra-frame compression process according to some embodiments of the present disclosure
  • FIG. 10 is an exemplary patch-matching-based motion prediction method according to some embodiments of the present disclosure.
  • FIG. 11 is an exemplary shared data-based compressing method according to some embodiments of the present disclosure.
  • FIG. 12 illustrates an exemplary procedure of FIG. 11 according to some embodiments of the present disclosure
  • FIG. 13 illustrates a reconstruction method of the video clip according to some embodiments of the present disclosure
  • FIG. 14 illustrates an exemplary procedure of FIG. 13 according to some embodiments of the present disclosure
  • FIG. 16 illustrates an exemplary multi-graph-combined-loss training according to some embodiments of the present disclosure
  • FIG. 17 illustrates an exemplary procedure of the first computation graph G1 according to some embodiments of the present disclosure
  • FIG. 22 illustrates an exemplary temporal module determining method according to some embodiments of the present disclosure
  • light received by a camera may be read out as raw-bayer data by image signal processing (ISP) chip.
  • ISP image signal processing
  • Raw-bayer data must be read-out of the camera using parallel or serial streaming as shown in FIG. 1.
  • FIG. 1 shows an example of original raw-bayer picture according to some embodiments of the present disclosure. As shown in FIG. 1, a raw-bayer picture is in shape of [2048, 3840] , and each pixel may have a corresponding pixel value. The pixel values may be read out in sequence by the camera head after capturing the frame.
  • the read-out data from a focal plane may be in a raster format, meaning rows are read out in sequence.
  • the input pixel values (raw-bayer data) is in shape of [2048, 3840] .
  • the [2048, 3840] original raw-bayer picture is compressed to an integer array of the shape [256, 480, 4] which will be described in Fig. 6. Pixels also correspond to different colors, typically red, green and blue, but the color values may be typically mosaicked across the sensor so that a given pixel corresponds to a given known color.
  • intra-frame compression may be performed on the raw-bayer data streams.
  • FIG. 2 illustrates an intra-frame compressing method according to some embodiments of the present disclosure.
  • the intra-frame compressing method may be implemented in electronics connected to the camera head.
  • intra-frame compression may be performed by compressing each group of raw pixel values into an intra-frame-compressive-sampling (ICS) frame with a compressing kernel.
  • the compression may be compressing each portion of the group of raw pixel values into an integer with the compressing kernel, wherein each portion of the group of raw pixel values corresponds to a section of the frame.
  • a section may be a patch or a segment which will be described in the frame strategies.
  • ICS frames include a first ICS-frame and a number of remaining ICS frames (R-ICS frames) after the first ICS-frame in time.
  • the compressing kernel may have Ncomp ICS-channels and Ncomp may be an integer not smaller than 1.
  • the sequence may be divided row by row.
  • Various 1D kernels or 1D integer vectors have been developed, including [128, 1, 4] , [32, 1, 4] .
  • combinations of different convolutional-1D kernels for different rows in the raw-bayer data may be used to control the total compression ratio of a frame.
  • This division way of the sequence of pixels uses less buffer size than that of the 2D patch division way, because pixel values from different rows/segments do not need to be buffered while the incoming pixel values can be processed as segments.
  • FIG. 3 shows a convolution process as described in 204 according to some embodiments of the present disclosure.
  • an array of pixels with dimension of 4*4 may be compressed into one integer with a compressing kernel.
  • the compressing kernel may also have a dimension of 4*4.
  • each stack includes a pre-defined number of patch-subtracted ICS-frames.
  • the numbers of patch-subtracted ICS-frames in each stack may be equal to each other.
  • a video clip consists of 100 frames may be compressed into 100 ICS frames (a first ICS frame and 99 R-ICS frames) , quantization may be performed to the 100 ICS frames (including a quantized first ICS frame and 99 QR-ICS frames) , then for each ICS-channel, patch-matching subtraction may be performed in 99 sub-QR-ICS frames relative to a sub-quantized first ICS-frame and generating 99 patch-subtracted ICS-frames, then the 99 patch-subtracted ICS-frames may be grouped into 33 stacks (total number of stacks is 33*Ncomp) , wherein each stack includes 3 patch-subtracted ICS-frames.
  • FIG. 8 shows an exemplary procedure of FIG. 7 according to some embodiments of the present disclosure.
  • patch-subtracted ICS-frames (COMP_SMP in each ICS-channel, with dimension [N x , N y ] ) may be grouped into stacks, and there are Nfp patch-subtracted ICS-frames in each stack. Pixel values data in each stack may be expressed as COMP_SMPS.
  • patch-matching-based motion prediction may be performed based on hierarchical patch searching.
  • FIG. 10 is an exemplary patch-matching-based motion prediction method according to some embodiments of the present disclosure.
  • the hierarchical patch searching may be described as the following steps 1006-1012, and the hierarchical patch searching may be performed in each relative patch.
  • the lowest square difference is smaller than a defined threshold.
  • the lowest square difference is one of the multi-square differences.
  • a target patch in the searching frame with lowest square difference may be determined as a reference patch, and the relative patch may be determined as a matched patch.
  • the relative patch may be defined as the matching frame and the target patch may be defined as the searching frame, then repeat 1004 to do hierarchical patch searching.
  • the hierarchical patch searching may be ended if a reference patch is determined or if patch size of the relative patch is not larger than the defined minimal patch size. For all relative patches, doing step 1006-1012. As a result, one or more matched patches in a sub-QR-ICS frame may be determined, and for each matched patch, a corresponding motion vector may be determined.
  • the stride size and the patch size of the relative patch may have positive correlation. For example, when the relative patch has a large patch size, the stride size may be big; while when the relative patch has a small patch size, the stride size may be small. Generally, the stride size is a defined integer not smaller than 2, and the stride size may be 1 only when the relative patch cannot be divided any more.
  • FIG. 11 is an exemplary shared data-based compressing method according to some embodiments of the present disclosure. Specifically, FIG. 11 shows a process of step 706.
  • shared data may be determined by convoluting patch-subtracted ICS-frames in the ICS-channel with a first kernel. Shared data may represent similar data among the patch-subtracted ICS-frames in a stack.
  • quantized shared data may be determined by quantizing values in the shared data to integers of pre-defined bit depth with a first scaling factor.
  • the quantization may include two steps. Firstly, values in the shared data may be scaled (reduce bit depth) to suit the range of n-bit integers with a first scaling factor. For example, values in the shared data may be multiplied by the first scaling factor. The first scaling factor may be an integer or a proper fraction. Secondly, scaled values of the shared data may be integerized to integers.
  • the quantized shared data may be rescaled to rescaled-quantized shared data (RQ-shared data) .
  • the quantized shared data may be rescaled by dividing values of the quantized shared data with the first scaling factor.
  • the rescaling operation may bring quantization loss. For example, a pixel with value 23 in the shared data may be scaled to 11.5 with a first scaling factor 1/2, and then be integerized to integer 12. Integer 12 then may be rescaled to integer 24 with the first scaling factor 1/2, which brings quantization loss between integer 24 and integer 23, wherein the error of the convolution and deconvolution has not been taken into account.
  • the RQ-shared data may be reshaped to reshaped RQ-shared data (RRQ-shared data) by performing deconvolution with a second kernel.
  • a stack-residual frame may be determined by subtracting RRQ-shared data from the patch-subtracted ICS-frame.
  • method in FIG. 11 may be further simplified.
  • Shared data in each stack corresponding to each ICS-channel may be determined by convoluting a weighted sum frame with a first kernel.
  • the weighted sum frame may be determined by doing weightedsummation of values in a same location of the patch-subtracted ICS-frames with weighted-sum parameters. And convolution of one weighted sum frame takes much less power and memory than convolution of multi-patch-subtracted ICS-frames.
  • entropy encoding may be performed to the quantized-shared data after step 1102. Secondly, do rescaling as described in 1108. In some embodiments, entropy encoding may be performed to the quantized-compressed stack-residual frames after step 1112. The entropy encoded quantized-compressed stack-residual frames may be stored for decoding. In some embodiments, entropy encoding may be performed to the motion vectors shared among ICS-channels.
  • Entropy encoding is an operation before transmission or storage, and the entropy encoded-quantized-shared data corresponding to each ICS-channel, the entropy encoded quantized-compressed stack-residual frames corresponding to each ICS-channel and entropy encoded-motion vectors corresponding to each stack shared among ICS-channels are stored for decoding/decompression.
  • quantized first ICS-frame may be also stored for adding matched patches to reconstruct the multi-frames of the video clip.
  • data in same type may be quantized shared data, or quantized-compressed stack-residual frames, or motion vectors . etc., while same type of data originated from different frames have difference, they share statistical similarity of distribution of values (like different Gaussian-distribution peaks with overlaps and/or close to each other) .
  • the global dictionary may be used to code values.
  • Smem_Q may be rescaled to Smem_Q_rsc (with a dimension of [N x /kresx, N y /kresy, ncomp_sm] )
  • Smem_Q_rsc may be reshaped to SMem_rs (with a dimension of [N x , N y ) by performing deconvolution to Smem_Q_rsc with a second kernel (with a dimension of [ksmx, ksmy, ncomp_sm] ) .
  • FIG. 13 illustrates a reconstruction method of the video clip according to some embodiments of the present disclosure.
  • each quantized-compressed stack-residual frame may be rescaled to RQ-compressed stack-residual frame, and quantized shared data may be rescaled to RQ-shared data.
  • the RQ-shared data may be determined based on the first scaling factor as described in 1106.
  • Each RQ-compressed stack-residual frame may be determined by dividing quantized-compressed stack-residual frame with the second scaling factor.
  • a second decompressed ICS-frame may be determined by adding corresponding RRQ-shared data and one or more corresponding matched patches with stored motion vectors to the first decompressed ICS-frame.
  • the step 1312 may be divided into two steps: rescaling the third decompressed ICS-frame to a fourth decompressed ICS-frame, and decompressing the fourth decompressed ICS-frame to a reconstructed frame.
  • SMem_rs may be added into COMP_sres_i_D to determine COMP_D, wherein COMP_D has a dimension of [N x , N y ] .
  • SMem_rs is determined based on entropy decoding (with the global dictionary) , rescaling (with the first scaling factor) and reshaping (with the second kernel) .
  • reconstructed frame may be determined by performing deconvolution to CPMP_D_all_chans with a decompressing kernel and a quality-improved neural network (QINN) , wherein CPMP_D_all_chans is a collection of values in all ICS-channels, and COMP_D_all_chans has a dimension of [N x , N y , Ncomp] , and the decompressing kernel has a dimension of [k x , k y , Ncomp] , and the reconstructed frame has a dimension of [N X , N Y ] .
  • QINN quality-improved neural network
  • a plurality of groups of sample raw pixel values may be read out, wherein each group of sample raw pixel values correspond to a frame.
  • the plurality of groups of sample raw pixel values may be used for sample-based training.
  • intra-frame compression may be performed by compressing each group of sample raw pixel values into a sample ICS frame with an initial compressing kernel, wherein the sample ICS frames including a sample first ICS-frame and sample R-ICS frames.
  • FIG. 19 illustrates an exemplary procedure of the third computation graph G3 according to some embodiments of the present disclosure.
  • FIG. 20 illustrates an exemplary procedure of the fourth computation graph G4 according to some embodiments of the present disclosure.

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Abstract

La présente invention concerne un procédé et un appareil de compression. Les étapes du procédé de compression consistent : à lire une pluralité de groupes de valeurs de pixels bruts à partir d'une tête de caméra, chaque groupe de valeurs de pixels bruts correspondant à une trame de la séquence vidéo; à faire une compression intratrame en compressant chaque groupe de valeurs de pixels bruts dans une trame d'échantillonnage compressif intratrame (ICS) avec un noyau de compression; à quantifier la première trame ICS, et à quantifier des trames R-ICS en trames QR-ICS, la première trame ICS quantifiée comprenant Ncomp premières trames ICS sous-quantifiées et chaque première trame ICS sous-quantifiée correspondant à un canal ICS de la première trame ICS quantifiée; pour des trames sous-QR-ICS correspondant à chaque canal ICS, à faire une soustraction d'appariement de pièces dans des trames sous-QR-ICS portant sur une première trame ICS sous-quantifiée correspondant audit canal ICS, et à générer des trames ICS soustraites par pièces; pour des trames ICS soustraites par pièces correspondant à chaque canal ICS, à regrouper les trames ICS soustraites par pièces en piles, chaque pile incluant un nombre prédéfini de trames ICS soustraites par pièces; pour des trames ICS soustraites par pièces dans chaque pile correspondant à chaque canal ICS, à déterminer des données partagées, les données partagées représentant des données similaires parmi les trames ICS soustraites par pièces, et à déterminer des trames résiduelles de piles sur la base des données partagées.
PCT/CN2019/110470 2019-10-10 2019-10-10 Procédé et appareil de compression de séquence vidéo WO2021068175A1 (fr)

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CN108632625A (zh) * 2017-03-21 2018-10-09 华为技术有限公司 一种视频编码方法、视频解码方法和相关设备
CN109587502A (zh) * 2018-12-29 2019-04-05 深圳市网心科技有限公司 一种帧内压缩的方法、装置、设备及计算机可读存储介质

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US20140212046A1 (en) * 2013-01-31 2014-07-31 Sony Corporation Bit depth reduction techniques for low complexity image patch matching
CN108022270A (zh) * 2016-11-03 2018-05-11 奥多比公司 使用基于预言的概率采样的图像补丁匹配
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