WO2012142731A1 - Method and device for lossy compress-encoding data and corresponding method and device for reconstructing data - Google Patents

Method and device for lossy compress-encoding data and corresponding method and device for reconstructing data Download PDF

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WO2012142731A1
WO2012142731A1 PCT/CN2011/000705 CN2011000705W WO2012142731A1 WO 2012142731 A1 WO2012142731 A1 WO 2012142731A1 CN 2011000705 W CN2011000705 W CN 2011000705W WO 2012142731 A1 WO2012142731 A1 WO 2012142731A1
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coefficients
post
data
compress
processing method
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French (fr)
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Wenfei JIANG
Zhibo Chen
Fan Zhang
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Technicolor China Technology Co Ltd
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Technicolor China Technology Co Ltd
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Priority to KR1020187018419A priority patent/KR101952709B1/en
Priority to JP2014505474A priority patent/JP5838258B2/en
Priority to US14/113,005 priority patent/US9288453B2/en
Priority to KR1020137027392A priority patent/KR101832792B1/en
Priority to KR1020207021516A priority patent/KR102231522B1/en
Priority to RS20190871A priority patent/RS58952B1/en
Priority to PL19180800.5T priority patent/PL3573337T3/en
Priority to CN201710795514.4A priority patent/CN107454409B/en
Priority to KR1020217008109A priority patent/KR102375037B1/en
Priority to EP11863936.8A priority patent/EP2700234B1/en
Priority to PCT/CN2011/000705 priority patent/WO2012142731A1/en
Priority to CN201180071804.5A priority patent/CN103748876B/en
Priority to PT11863936T priority patent/PT2700234T/en
Priority to HUE11863936A priority patent/HUE044124T2/en
Priority to DK11863936.8T priority patent/DK2700234T3/en
Priority to CN201710795511.0A priority patent/CN107529059B/en
Priority to ES19180800T priority patent/ES2924886T3/en
Priority to KR1020197034953A priority patent/KR102139199B1/en
Priority to ES11863936T priority patent/ES2736003T3/en
Priority to KR1020197005173A priority patent/KR102051013B1/en
Priority to EP19180800.5A priority patent/EP3573337B1/en
Priority to PL11863936T priority patent/PL2700234T3/en
Priority to KR1020187005052A priority patent/KR101874466B1/en
Priority to CN201710795835.4A priority patent/CN107454410B/en
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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
    • H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
    • 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
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/146—Data rate or code amount at the encoder output
    • H04N19/147—Data rate or code amount at the encoder output according to rate distortion criteria
    • 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
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/124—Quantisation
    • 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
    • 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
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/132—Sampling, masking or truncation of coding units, e.g. adaptive resampling, frame skipping, frame interpolation or high-frequency transform coefficient masking
    • 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
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/18—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a set of transform coefficients
    • 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
    • H04N19/46—Embedding additional information in the video signal during the compression process
    • H04N19/467—Embedding additional information in the video signal during the compression process characterised by the embedded information being invisible, e.g. watermarking
    • 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
    • H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
    • 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
    • H04N19/70—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
    • 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
    • H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
    • H04N19/86—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving reduction of coding artifacts, e.g. of blockiness
    • 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
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
    • H04N19/91—Entropy coding, e.g. variable length coding [VLC] or arithmetic coding

Definitions

  • the invention is made in the field of lossy compress- encoding data comprising at least one of image data and audio data.
  • Lossy compress-encoding tries to represent data, e.g. audio or video data, with as few bits as possible while at the same time trying to allow the data to be reconstructed from the lossy compress-encoded representation as good as possible .
  • a rate-distortion cost function is defined. Minimizing this function then allows for a lossy compression scheme which delivers the best trade-off between encoding costs in terms of bitrate and information loss in terms of distortion of reconstructed data with respect to original data.
  • Reconstructing the data may comprise post-processing. That is, first a preliminary reconstruction of the data is generated using the information contained in the compress- encoded data. Then, a post-processing _me_thod_is applied f-ox- regaining that part of information which was removed from the original data by lossy compression.
  • An example thereof is the removal of film grain noise from image data in course of lossy compression and subsequent addition of simulated film grain noise to a preliminary reconstruction obtained from the lossy compress-encoded image data.
  • Another exemplary source of distortion is quantization.
  • the data is commonly predicted using already encoded data.
  • compress-encoded using, e.g., Huffman coding or arithmetic encoding .
  • Quantization can be non-linear such that the coefficients are thinned out or sparsified, i.e. only a sub-set of the frequency information is maintained. This is similar or identical to linear quantization combined with
  • CS-flag An additional flag, denoted as CS-flag, is employed to mark the selected coding mode.
  • the decoder reads the CS-flag and then executes the appropriate reconstruction algorithm corresponding to the CS mode or the normal modes.
  • the inventors of the current invention identified the problem that transmission of a flag whether to perform post processing like total variation (TV) regularization results in significant overhead in the bit-stream, especially for the low bit-rate compression. This problem even intensifies in case several post-processing methods can be used and thus have to be signalled.
  • TV total variation
  • Said method further comprises modifying said quantized coefficients for minimizing rate-distortion cost wherein distortion is determined using a post-processed reconstruction of the data, the post-processed
  • the post-processing method is that one of n>l different predetermined post processing method candidates whose position in an
  • predetermined order of arrangement of the post processing method candidates equals a remainder of division, by n, of a sum of the modified coefficients. Doing so removes the overhead of flags in the bit stream.
  • these steps are executed using processing means adapted correspondingly.
  • the inventors ' further propose non-transitory means at least partly dedicated for at least one of storage and
  • reconstructing data comprising at least one of image data and audio data, comprises compress-decoding coefficients, using processing means for determining a preliminary reconstruction of the data using the compress-decoded coefficients, and determining a reconstruction of the data by post-processing the preliminary reconstruction using that one of n>l different predetermined post processing method candidates whose position in an order of arrangement of the post processing method candidates equals a remainder of division, by n, of a sum of the compress-decoded coefficients.
  • FIG. 1 depicts an exemplary flow chart of the encoding procedure according to the invention
  • Fig. 2 depicts an exemplary flow chart of embedding
  • Fig. 3 depicts an exemplary flow chart of the decoding procedure according to the invention.
  • the invention may be realized on any electronic device comprising a processing device correspondingly adapted.
  • the invention may be realized in a television, a mobile phone, a personal computer, a digital still camera, a digital video camera, an mp3-player, a navigation system or a car audio system.
  • the invention is used for encoding an image composed of image pixels.
  • a residual between a block of image pixels yet- to-be encoded and a prediction of said block is determined.
  • the prediction is determined using already encoded image pixels.
  • a transformation from spatial domain to frequency domain such as discrete cosine transform, is applied on the residual. From the transformation result a sequence of quantized coefficients is generated by
  • quantization and scanning according to a scan order wherein it is unimportant whether quantization or scanning occurs first .
  • quantized coefficients for further modification those are selected which are of reduced relevancy for the human visual system, e.g. coefficients associated with frequencies above a threshold associated with human
  • each determined coefficient is the only non-zero valued coefficient in the corresponding subsequence .
  • All the determined coefficients may be set to Zero which leads to compression without impacting image quality significantly.
  • rate-distortion cost optimization can be used for identifying, among the determined coefficients, and setting to Zero those which, when set to zero, lead to an improvement of rate-distortion cost. Doing so provides an adaptive compressive sensing based video coding scheme which adaptively selects the
  • Rate-distortion cost optimization can take into account one or more post-processing methods like total variation regularization, also called total variation minimization, or l j minimization, also called Norm-1 minimization, the one or more post-processing methods being arranged, together with a dummy post-processing method representative of no post processing, in an order, i.e. each postprocessing has an associated ordinal number.
  • modification of the determined coefficients can be made such that a remainder of division, by n, of a sum of the all coefficients including the modified ones is equal to the ordinal number of that post processing method which is best suited for minimization of the distortion.
  • a preliminary suitable post-processing is determined, then coefficients are modified to achieve said equality, in response to which it is either verified that the preliminary determined post-processing is still suitable, or a new preliminary suitable post-processing is determined which triggers further modification.
  • a post processing method being the one of n>l different, sorted and predetermined post processing method candidates which minimizes distortion when used for reconstructing the block using said encoded coefficients and said prediction. That is, information is sent in a covert communication channel whether and/or which post processing improves image restoration best.
  • a varying quantization parameter can be used for
  • At least one of the positive threshold and the minimum positive number of Zero-valued coefficients per sub-sequence can vary too in dependency on the quantization parameter.
  • the decoded coefficients are de-quantized and inverse transformed and, for
  • An exemplary embodiment of an encoding device scans the coefficients after DCT and quantization of each block, and finds isolated small coefficients (e.g., an isolated 1 in the middle of a number of successive zeros) which do not contribute to the reconstruction quality significantly. Then such coefficients are discarded since this probably degrades the quality slightly but reduces the bit-rate much. Thus, only the significant coefficients are selected and written into the bit-stream.
  • isolated small coefficients e.g., an isolated 1 in the middle of a number of successive zeros
  • the exemplary embodiment of the encoding device is capable of choosing adaptively among 1 1 minimization, total variation minimization and skipping post-processing and indicating the choice by Covert
  • post-processing modes e.g. total variation (TV) minimization
  • TV total variation
  • the exemplary embodiment of the encoding device can process each block and computes the distortion, e.g. by computing PSNR. If the quality
  • the modification preferably is being carried out on higher frequency components above a threshold .
  • sum of the coefficients can be controlled by not discarding all of the small and isolated coefficients and/or by not discarding but only reducing some or all of these
  • the tested exemplary encoder goes through these steps: Given the quantization parameter (QP) of H.264 compression, the tested exemplary encoder calculates at least the parameters Threshold_Run, Threshold_Level , TV_lambda. These calculated parameters satisfy that the Adaptive Sensing Operator or TV Regularization module can achieve the optimal compression at each QP .
  • the parameters Given the quantization parameter (QP) of H.264 compression, the tested exemplary encoder calculates at least the parameters Threshold_Run, Threshold_Level , TV_lambda. These calculated parameters satisfy that the Adaptive Sensing Operator or TV Regularization module can achieve the optimal compression at each QP .
  • the parameters Given the quantization parameter (QP) of H.264 compression, the tested exemplary encoder calculates at least the parameters Threshold_Run, Threshold_Level , TV_lambda. These calculated parameters satisfy that the Adaptive Sensing Operator or TV Regularization module can achieve the optimal compression at
  • Threshold_Level and Threshold_Run have been optimized for each QP using a training set of various video sequences.
  • the tested exemplary encoder obtains the residual data by subtracting the inter/intra prediction from the original block Forg, which is then transformed, quantized, and arranged in a sequence by scanned according to a
  • predetermined scan order e.g. the various frequency components are zigzag reordered.
  • An Adaptive Sensing Operator ASO realized in the tested exemplary encoder then tries to represent the frame as accurate as possible at a relatively low bit cost. To do so, the coefficients with small magnitudes that consume many bits are examined as to whether they can be discarded. This is achieved by investigating the sequence of the coefficients. For each Coefficient C that stays ahead of a successive zeros and after b successive zeros, if C ⁇
  • Threshold_Level and a + b ⁇ Threshold_Run, C is candidate to be set to zero.
  • Rate-Distortion optimization is employed by Adaptive
  • Sensing Operator ASO to determine whether to set to zero the detected candidate coefficients.
  • Sensing Operator ASO is adapted for excluding the beginning 25 coefficients which- are sensitive to human eye from being set to zero.
  • the tested exemplary encoder therefore tentatively applies TV regularization on the reconstructed block Free, obtaining block FTV. Then, the exemplary encoder evaluates the quality of Free and FTV by comparison with the original data Forg. If the
  • the tested exemplary - encoder signals TV regularization to be used at decoder side for output as well for prediction.
  • the tested exemplary encoder therefore embeds the message of whether to use TV regularization into the bit-steam. If TV regularization makes quality better and thus is
  • the sum of coefficients shall be odd; otherwise, the sum even.
  • the tested exemplary encoder then computes the sum of coefficients and checks whether the parity follows the above rule, i.e. whether parity fits to applicability of post processing according to a rule present in encoder and decoder. If not, the tested
  • exemplary encoder modifies, in module MOD, one of the remaining non-zero coefficients or one of the discarded small and isolated high frequency coefficients by 1 or -1 to meet this requirement.
  • the frequency of the modified coefficient shall be as high as possible.
  • the frequency of the modified coefficient shall be as high as possible. Therefore, rate-distortion cost minimization can be used to determine which of the different frequency components to modify as well as how to modifv . As search space for this determination is large, the following prioritization is applied in the exemplary encoder tested:
  • the block reconstructed from the finally resulting coefficients is saved in the buffer as a candidate for prediction of blocks to-be-encoded; and the finally resulting coefficients are entropy encoded and written into the bit-stream, on a non-transitory storage medium or are transmitted as a signal.
  • An exemplary embodiment of a device for reconstructing data encoded as such receives the encoded coefficients and decodes them. Then the device determines parity of the coefficients. Parity being odd informs the exemplary decoder that TV regularization can be applied beneficially. Parity being even informs the exemplary decoder that computational effort of TV regularization can be omitted without impairing image quality. Next, the exemplary decoder applies inverse quantization and inverse transformation on the coefficients. The resulting
  • coefficients are arranged in a block corresponding to the predetermined scan order used at encoder side. This results in a reconstructed residual which is combined with the prediction resulting in a decoded block FDEC. Finally, post processing is applied or omitted depending on parity of the decoded coefficients.

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Abstract

The invention proposes modification of quantized coefficients for signalling of a post-processing method. Therefore, it is proposed a method for lossy compress- encoding data comprising at least one of image data and audio data. Said method comprises determining quantized coefficients using a quantization of a discrete cosine transformed residual of a prediction of said data. Said method further comprises modifying said quantized coefficients for minimizing rate-distortion cost wherein distortion is determined using a post-processed reconstruction of the data, the post-processed reconstruction being post-processed according to a post¬ processing method, and compress-encoding said modified coefficients. In said proposed method, the post-processing method is that one of n>l different predetermined post processing method candidates whose position in an predetermined order of arrangement of the post processing method candidates equals a remainder of division, by n, of a sum of the modified coefficients. Doing so removes the overhead of flags in the bit stream.

Description

Method and Device for lossy compress-encoding data and corresponding method and device for reconstructing data
TECHNICAL FIELD
The invention is made in the field of lossy compress- encoding data comprising at least one of image data and audio data.
BACKGROUND OF THE INVENTION Lossy compress-encoding tries to represent data, e.g. audio or video data, with as few bits as possible while at the same time trying to allow the data to be reconstructed from the lossy compress-encoded representation as good as possible . To achieve this goal, commonly a rate-distortion cost function is defined. Minimizing this function then allows for a lossy compression scheme which delivers the best trade-off between encoding costs in terms of bitrate and information loss in terms of distortion of reconstructed data with respect to original data.
Reconstructing the data may comprise post-processing. That is, first a preliminary reconstruction of the data is generated using the information contained in the compress- encoded data. Then, a post-processing _me_thod_is applied f-ox- regaining that part of information which was removed from the original data by lossy compression.
An example thereof is the removal of film grain noise from image data in course of lossy compression and subsequent addition of simulated film grain noise to a preliminary reconstruction obtained from the lossy compress-encoded image data.
Another exemplary source of distortion is quantization. For compressing video or audio data,- the data is commonly predicted using already encoded data. The residual
remaining form prediction is the transformed from spatial and/or temporal domain to frequency domain using, for instance, discrete cosine transformation or wavelet transformation. The resulting coefficients then are quantized. Finally, the quantized coefficients are
compress-encoded using, e.g., Huffman coding or arithmetic encoding .
Quantization can be non-linear such that the coefficients are thinned out or sparsified, i.e. only a sub-set of the frequency information is maintained. This is similar or identical to linear quantization combined with
modification. E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans, on Information Theory, vol. 52, pp. 489 - 509, Feb. 2006, . proved theoretically that, anyway, image can be exactly reconstructed from such sub-set using appropriate postprocessing .
Y. Zhang, S. Mei, Q. Chen, and Z. Chen, "A novel
image/video coding method based on compressed sensing theory," In Proceedings of IEEE ICASSP, pp. 1361-1364, Apr. 2008, proposed a method of image/video coding by employing transform coefficient subsampling and total variation (TV) minimization based post processing of preliminary block reconstruction in the residue domain.
M.R. Dadkhah, S. Shirani, M.J. Deen, "Compressive sensing with modified total variation minimization algorithm", In Proceedings of IEEE ICASSP, pp. 1030 - 1033, Mar. 14 - 19, 2010, mention exploiting Norm-1 post-processing for image reconstruction .
Another example of the use of total variation-minimization- based post processing can be found in T.T.Do, X. Lu, J. Sole, "Compressive sensing with adaptive pixel domain reconstruction for block-based video coding", In
Proceedings of ICIP, pp. 3377 - 3380 Sep. 26-29, 2010.
Therein, a video encoder is proposed which selects between a new coding mode using adaptive total variation
minimization block recovery and existing H.264 modes. An additional flag, denoted as CS-flag, is employed to mark the selected coding mode. The decoder reads the CS-flag and then executes the appropriate reconstruction algorithm corresponding to the CS mode or the normal modes. SUMMARY OF THE INVENTION
The inventors of the current invention identified the problem that transmission of a flag whether to perform post processing like total variation (TV) regularization results in significant overhead in the bit-stream, especially for the low bit-rate compression. This problem even intensifies in case several post-processing methods can be used and thus have to be signalled.
The inventors realized that the modification of quantized coefficients can be used for signalling the post-processing method.
Therefore, it is proposed a method according to claim 1 for lossy compress-encoding data comprising at least one of image data and audio data. Said method comprises
determining quantized coefficients using a quantization of a discrete cosine transformed residual of a prediction of said data. Said method further comprises modifying said quantized coefficients for minimizing rate-distortion cost wherein distortion is determined using a post-processed reconstruction of the data, the post-processed
reconstruction being post-processed according to a postprocessing method, and compress-encoding said modified coefficients. In said proposed method, the post-processing method is that one of n>l different predetermined post processing method candidates whose position in an
predetermined order of arrangement of the post processing method candidates equals a remainder of division, by n, of a sum of the modified coefficients. Doing so removes the overhead of flags in the bit stream.
In an embodiment these steps are executed using processing means adapted correspondingly.
The inventors' further propose non-transitory means at least partly dedicated for at least one of storage and
transmission of a compress-encoded data comprising at least one of image data and audio data, the data being compress- encoded according to the said proposed method for lossy compress-encoding .
A corresponding method according to claim 7 for
reconstructing data comprising at least one of image data and audio data, comprises compress-decoding coefficients, using processing means for determining a preliminary reconstruction of the data using the compress-decoded coefficients, and determining a reconstruction of the data by post-processing the preliminary reconstruction using that one of n>l different predetermined post processing method candidates whose position in an order of arrangement of the post processing method candidates equals a remainder of division, by n, of a sum of the compress-decoded coefficients.
Furthermore, corresponding devices according to claims 12 and 13 are proposed. The features of further advantageous embodiments are specified in the dependent claims.
BRIEF DESCRIPTION OF THE DRAWINGS Exemplary embodiments of the invention are illustrated in the drawings and are explained in more detail in the following description. The exemplary embodiments are explained only for elucidating the invention, but not limiting the invention's disclosure or scope solely defined by the claims.
In the figures:
Fig. 1 depicts an exemplary flow chart of the encoding procedure according to the invention; Fig. 2 depicts an exemplary flow chart of embedding
covertly information on post processing in a bit stream; and
Fig. 3 depicts an exemplary flow chart of the decoding procedure according to the invention.
EXEMPLARY EMBODIMENTS OF THE INVENTION
The invention may be realized on any electronic device comprising a processing device correspondingly adapted. For instance, the invention may be realized in a television, a mobile phone, a personal computer, a digital still camera, a digital video camera, an mp3-player, a navigation system or a car audio system. In an exemplary embodiment, the invention is used for encoding an image composed of image pixels. In said
embodiment a residual between a block of image pixels yet- to-be encoded and a prediction of said block is determined. The prediction is determined using already encoded image pixels. Next, a transformation from spatial domain to frequency domain, such as discrete cosine transform, is applied on the residual. From the transformation result a sequence of quantized coefficients is generated by
quantization and scanning according to a scan order wherein it is unimportant whether quantization or scanning occurs first .
Among the quantized coefficients, for further modification those are selected which are of reduced relevancy for the human visual system, e.g. coefficients associated with frequencies above a threshold associated with human
perceptive sensitivity. This ensures that subsequent modification does not lead to distortions of extreme saliency to the user. Then, among the selected coefficients, those are determined which are positive valued and do not exceed a positive threshold and which further are contained in contiguous sub-sequences of at least a positive number of Zero valued coefficients, i.e. each determined coefficient is the only non-zero valued coefficient in the corresponding subsequence .
All the determined coefficients may be set to Zero which leads to compression without impacting image quality significantly. Or, rate-distortion cost optimization can be used for identifying, among the determined coefficients, and setting to Zero those which, when set to zero, lead to an improvement of rate-distortion cost. Doing so provides an adaptive compressive sensing based video coding scheme which adaptively selects the
coefficients that are the most efficient in representing video frames. Rate-distortion cost optimization can take into account one or more post-processing methods like total variation regularization, also called total variation minimization, or lj minimization, also called Norm-1 minimization, the one or more post-processing methods being arranged, together with a dummy post-processing method representative of no post processing, in an order, i.e. each postprocessing has an associated ordinal number.
Thus, in an embodiment it is determined whether postprocessing improves the quality of the restored images as well as the post processing which improves quality the most .
Then, modification of the determined coefficients can be made such that a remainder of division, by n, of a sum of the all coefficients including the modified ones is equal to the ordinal number of that post processing method which is best suited for minimization of the distortion. For making easier achieving of this equality, even coefficients associated with frequencies below or at the perceptivity threshold can be modified. Further or as an alternative, achieving of this equality can be made in an iterative fashion, i.e. a preliminary suitable post-processing is determined, then coefficients are modified to achieve said equality, in response to which it is either verified that the preliminary determined post-processing is still suitable, or a new preliminary suitable post-processing is determined which triggers further modification. In practice, it was found that a single iteration was sufficient in the rare cases where the verification of a first preliminary determined post-processing failed.
Finally, there is encoded the resulting coefficients together with information allowing a decoder to determine the prediction.
Doing so enables signalling, in a bit stream comprising compress encoded quantized coefficients determined using a quantization of a discrete cosine transformed residual of a prediction of a block of pixels of an image, a post processing method being the one of n>l different, sorted and predetermined post processing method candidates which minimizes distortion when used for reconstructing the block using said encoded coefficients and said prediction. That is, information is sent in a covert communication channel whether and/or which post processing improves image restoration best.
A varying quantization parameter can be used for
quantization. In that case, at least one of the positive threshold and the minimum positive number of Zero-valued coefficients per sub-sequence can vary too in dependency on the quantization parameter.
For reconstructing a block of pixels of an image encoded in such way, coefficients and information allowing a decoder to determine the prediction are decoded. Next, a remainder of division, by a predetermined positive number n, of a sum of the decoded coefficients is determined. For
reconstructing the residual, the decoded coefficients are de-quantized and inverse transformed and, for
reconstructing the prediction the decoded information is used. Then, prediction and residual are combined. The remainder of the divison is used for selecting a candidate post-processing which is then applied on the combination of reconstructed residual and reconstructed prediction for determining the final reconstruction of the block.
An exemplary embodiment of an encoding device scans the coefficients after DCT and quantization of each block, and finds isolated small coefficients (e.g., an isolated 1 in the middle of a number of successive zeros) which do not contribute to the reconstruction quality significantly. Then such coefficients are discarded since this probably degrades the quality slightly but reduces the bit-rate much. Thus, only the significant coefficients are selected and written into the bit-stream.
Additionally or alternatively, the exemplary embodiment of the encoding device is capable of choosing adaptively among 11 minimization, total variation minimization and skipping post-processing and indicating the choice by Covert
Communication .
In many cases, post-processing modes, e.g. total variation (TV) minimization, work well on compensating the distortion caused by quantization and/or coefficient discarding, meanwhile, sometimes they fail. The exemplary embodiment of the encoding device can process each block and computes the distortion, e.g. by computing PSNR. If the quality
improves, it embeds the message of "to do TV
regularization" into the bit-stream in a covert
communication channel.
An instance of covert messages is that, if TV
regularization is required_at jthe _ decooLer_and—T_V—
regularization is the only available candidate post processing method besides no post processing, the sum of the coefficients shall be odd; otherwise, the sum shall be even. For the case of 3 available post-processing modes, modulus-3 will be used instead of parity-check. Since sum of coefficients not necessarily is odd in case TV regulari zation is useful and not necessarily is even in case no post processing is preferable, coefficients sometimes need to be modified. This is best done in a way which reduces bit rate and minimizes distortion resulting form such modification, i.e. not only ensures sum of modified coefficients having correct parity but further minimizes rate-distortion cost.
Since human eye is far more sensitive to variations in lower frequency components the modification preferably is being carried out on higher frequency components above a threshold .
And, since encoding small and isolated coefficients requires comparably many bits the modification preferably is being carried out on small and isolated high frequency components .
Therefore, in an embodiment also comprising discarding of small and isolated high frequency components, anyway, sum of the coefficients can be controlled by not discarding all of the small and isolated coefficients and/or by not discarding but only reducing some or all of these
coefficients.
The inventive principles set forth in- the claims were tested in an exemplary encoder built upon the H.264 codec. For simplicity, only 8x8 transform was used, however, the proposed method is also suitable for other block sizes such as 4x4. Furthermore, only TV regularization __was_ consi-dexed- for post-processing.
The tested exemplary encoder goes through these steps: Given the quantization parameter (QP) of H.264 compression, the tested exemplary encoder calculates at least the parameters Threshold_Run, Threshold_Level , TV_lambda. These calculated parameters satisfy that the Adaptive Sensing Operator or TV Regularization module can achieve the optimal compression at each QP . The parameters
Threshold_Level and Threshold_Run have been optimized for each QP using a training set of various video sequences.
The tested exemplary encoder obtains the residual data by subtracting the inter/intra prediction from the original block Forg, which is then transformed, quantized, and arranged in a sequence by scanned according to a
predetermined scan order, e.g. the various frequency components are zigzag reordered.
An Adaptive Sensing Operator ASO realized in the tested exemplary encoder then tries to represent the frame as accurate as possible at a relatively low bit cost. To do so, the coefficients with small magnitudes that consume many bits are examined as to whether they can be discarded. This is achieved by investigating the sequence of the coefficients. For each Coefficient C that stays ahead of a successive zeros and after b successive zeros, if C ≤
Threshold_Level and a + b ≥ Threshold_Run, C is candidate to be set to zero.
Rate-Distortion optimization is employed by Adaptive
Sensing Operator ASO to determine whether to set to zero the detected candidate coefficients. In consideration of subjective quality, the Adaptive
Sensing Operator ASO is adapted for excluding the beginning 25 coefficients which- are sensitive to human eye from being set to zero.
For each candidate coefficient actually set to zero the bits for a level value and a run-length value are saved without degrading the quality significantly. To alleviate the quality loss caused by quantization and coefficient dropping, TV minimization in principle is beneficial. But, although parameter TV_lambda has been optimized for a given quantization parameter based on various videos, there is still a possibility that TV regularization actually degrades quality even.
In a post-processing module PP, the tested exemplary encoder therefore tentatively applies TV regularization on the reconstructed block Free, obtaining block FTV. Then, the exemplary encoder evaluates the quality of Free and FTV by comparison with the original data Forg. If the
distortion of FTV is smaller, the tested exemplary - encoder signals TV regularization to be used at decoder side for output as well for prediction. The tested exemplary encoder therefore embeds the message of whether to use TV regularization into the bit-steam. If TV regularization makes quality better and thus is
required, the sum of coefficients shall be odd; otherwise, the sum even. The tested exemplary encoder then computes the sum of coefficients and checks whether the parity follows the above rule, i.e. whether parity fits to applicability of post processing according to a rule present in encoder and decoder. If not, the tested
exemplary encoder modifies, in module MOD, one of the remaining non-zero coefficients or one of the discarded small and isolated high frequency coefficients by 1 or -1 to meet this requirement.
For the sake of impact on visibility, the frequency of the modified coefficient shall be as high as possible. For the sake of bit rate, the frequency of the modified coefficient shall be as high as possible. Therefore, rate-distortion cost minimization can be used to determine which of the different frequency components to modify as well as how to modifv . As search space for this determination is large, the following prioritization is applied in the exemplary encoder tested:
If any discarded coefficients are odd: Restore the one associated with the lowest frequency or the one whose discarding resulted in greatest additional distortion.
Only, if the discarded coefficients are all even: Modify one of the discarded coefficients by ±1.
If no coefficient was discarded: Modify one of the nonzero coefficients by ±1.
It was determined advantageous if the absolute of the modified coefficient is reduced.
Since modification may affect the usefulness of TV
regularization, evaluation of TV regularization' s effect on distortion and modification of coefficients are re-done until parity of coefficients equals the preferable way of reconstruction .
Finally, the block reconstructed from the finally resulting coefficients is saved in the buffer as a candidate for prediction of blocks to-be-encoded; and the finally resulting coefficients are entropy encoded and written into the bit-stream, on a non-transitory storage medium or are transmitted as a signal.
An exemplary embodiment of a device for reconstructing data encoded as such receives the encoded coefficients and decodes them. Then the device determines parity of the coefficients. Parity being odd informs the exemplary decoder that TV regularization can be applied beneficially. Parity being even informs the exemplary decoder that computational effort of TV regularization can be omitted without impairing image quality. Next, the exemplary decoder applies inverse quantization and inverse transformation on the coefficients. The resulting
coefficients are arranged in a block corresponding to the predetermined scan order used at encoder side. This results in a reconstructed residual which is combined with the prediction resulting in a decoded block FDEC. Finally, post processing is applied or omitted depending on parity of the decoded coefficients.

Claims

CLAIMS :
1. Method for lossy compress-encoding data comprising at least one of image data or audio data, said method
comprising
- determining quantized coefficients using a quantization of a discrete cosine transformed residual of a
prediction of said data,
- modifying said quantized coefficients for minimizing rate-distortion cost wherein distortion is determined using a post -processed reconstruction of the data, the post-processed reconstruction being post-processed according to a post -processing method, and
- compress -encoding said modified coefficients wherein
- the post -processing method is that one of n>l different predetermined post processing method candidates whose position in an predetermined order of arrangement of the post processing method candidates equals a remainder of division, by n , of a sum of the modified coefficients.
2. The method of claim 1, wherein the reconstruction is determined using said modified coefficients and said prediction, said prediction being determined using already compress-encoded data and a reference to the already compress-encoded data is further compress-encoded.
3. The method of one of claims 1 or 2 , wherein the step of modifying said quantized coefficients comprises
- (a) Determining that a difference unequal to Zero exists between a remainder of division, by n , of a sum of the quantized coefficients and the position of that one of n different, ordered and predetermined post processing method candidates which minimizes distortion when used for reconstructing said block using said quantized coefficients and said prediction, and
- (b) modifying the quantized coefficients such that
overall modification equals the non-Zero difference.
4. The method of claim 3, further comprising repeating steps (a) and (b) using in each repetition of step (a) the modified coefficients resulting from immediately preceding execution of step (b) until existence of a difference unequal to Zero is not determined.
5. The method of one of claims 1-4, wherein the data and the quantized coefficients are arranged as two-dimensional blocks and modifying the coefficients comprises: determining a sequence of coefficients by: o scanning the quantized coefficients according to a scan order and using the sequence for determining those quantized coefficients which each :
• represents a frequency above a predetermined frequency threshold, · does not exceed a predetermined positive
threshold, and
• is the only non-Zero coefficient contained in contiguous subsequence of at least a predetermined positive number of quantized coefficients, and
- identifying, among the determined coefficients, those which, when set to zero, lead to minimization of rate- distortion cost and setting the identified coefficients to Zero.
6. The method of claim 5, further comprising determining a quantization parameter for quantization wherein at least one of the positive threshold and the positive number depends on the determined quantization parameter.
7. Method for reconstructing data comprising at least one of image data and audio data, said method comprising
- compress-decoding coefficients,
- determining a preliminary reconstruction of the data using the compress-decoded coefficients, and
- determining a reconstruction of the data by postprocessing the preliminary reconstruction using that one of n>l different predetermined post processing method candidates whose position in an order of arrangement of the post processing method candidates equals a remainder of division, by n, of a sum of the compress-decoded coefficients .
8. The method of one of claims 1-7, wherein one of the post processing method candidates is total variation
regulari zation .
9. The method of one of claims 1-9, wherein one of the post processing method candidates is 12 minimization.
10. The method of one of claims 1-10, wherein one of the post processing method candidates is a dummy post
processing method which does not process at all.
11. Device for lossy compress-encoding data comprising at least one of image data and audio data, comprising
- processing means adapted for determining quantized
coefficients using a quantization of a discrete cosine transformed residual of a prediction of said data,
- the processing means being further adapted for modifying said quantized coefficients for minimizing rate- distortion cost wherein distortion is determined using a reconstruction of the block post-processed according to a post-processing method,
- encoding means adapted for compress-encoding said
modified coefficients wherein
- the post -processing method used for distortion
determination is that one of n>l different predetermined post processing method candidates whose position in an predetermined order of arrangement of the post
processing method candidates equals a remainder of division, by n, of a sum of the modified coefficients.
12. Device for reconstructing data comprising at least one of image data and audio data, comprising - decoding means adapted for compress-decoding
coefficients, and
- processing means adapted for determining a preliminary reconstruction of the data using the compress-decoded coefficients, wherein
- the processing means are further adapted for determining a reconstruction of the block by post-processing the preliminary reconstruction using that one of n>l
different predetermined post processing method
candidates whose position in an order of arrangement of the post processing method candidates equals a remainder of division, by n , of a sum of the compress-decoded coefficients .
Means at least partly dedicated for at least one of storage and transmission of a compress -encoded data comprising at least one of image data and audio data, the data being compress-encoded according to the method of one of claims 1-6.
PCT/CN2011/000705 2011-04-22 2011-04-22 Method and device for lossy compress-encoding data and corresponding method and device for reconstructing data Ceased WO2012142731A1 (en)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014078985A1 (en) * 2012-11-20 2014-05-30 Thomson Licensing Method and apparatus for image regularization
WO2014149818A1 (en) * 2013-03-15 2014-09-25 Magnum Semiconductor, Inc. Apparatuses and methods for providing quantized coefficients for video encoding
US9491475B2 (en) 2012-03-29 2016-11-08 Magnum Semiconductor, Inc. Apparatuses and methods for providing quantized coefficients for video encoding
US9794575B2 (en) 2013-12-18 2017-10-17 Magnum Semiconductor, Inc. Apparatuses and methods for optimizing rate-distortion costs in video encoding
CN110262819A (en) * 2019-06-04 2019-09-20 深圳前海微众银行股份有限公司 A kind of the model parameter update method and device of federal study

Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9767212B2 (en) 2010-04-07 2017-09-19 Liveperson, Inc. System and method for dynamically enabling customized web content and applications
CN103947206B (en) 2011-10-14 2018-01-23 超威半导体公司 The method and apparatus of compression of images based on region
US8929446B1 (en) * 2014-03-14 2015-01-06 Faroudja Enterprises, Inc. Combiner processing system and method for support layer processing in a bit-rate reduction system
CN104270641B (en) * 2014-09-30 2018-12-14 杭州华为数字技术有限公司 The treating method and apparatus of transformation coefficient
KR102338980B1 (en) 2015-03-23 2021-12-13 삼성전자주식회사 Encoder for adjusting quantization coefficient to remove flicker and device including the same
JP6564315B2 (en) * 2015-12-04 2019-08-21 日本放送協会 Encoding device, decoding device, and program
CN112106335B (en) * 2018-04-30 2021-11-09 杜比国际公司 Method and system for streaming media data over a content distribution network
CN108683915B (en) * 2018-05-11 2020-08-07 北京奇艺世纪科技有限公司 Method and device for writing dQP value and electronic equipment
KR102777347B1 (en) 2018-11-30 2025-03-05 삼성전자주식회사 Image processing device and frame buffer compressor
CN118524208A (en) 2019-06-14 2024-08-20 韩国电子通信研究院 Quantization matrix encoding/decoding method and apparatus, and recording medium storing bit stream
EP3997588A1 (en) * 2019-08-21 2022-05-18 Siemens Industry Software NV Method for storing data to and retrieving data from at least one data storage, system, use, computer program, and computer readable medium
EP4042695A1 (en) 2019-10-02 2022-08-17 V-Nova International Limited Use of embedded signalling for backward-compatible scaling improvements and super-resolution signalling
US11997570B1 (en) 2020-05-14 2024-05-28 Trackonomy Systems, Inc. Optimized context-based communications compression for IOT systems and networks
KR102315376B1 (en) 2021-03-16 2021-10-20 모멘티브퍼포먼스머티리얼스코리아 주식회사 Silicone-based composition and cured product thereof
CN115209146B (en) * 2021-04-13 2026-03-03 腾讯科技（深圳）有限公司 Video encoding and decoding method and device, computer readable medium and electronic equipment
CN113489978A (en) * 2021-05-27 2021-10-08 杭州博雅鸿图视频技术有限公司 Distortion optimization quantization circuit for AVS3

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5321522A (en) * 1993-07-19 1994-06-14 Xerox Corporation ADCT compression with minimum compression ratio
WO1999029115A1 (en) * 1997-12-01 1999-06-10 Rockwell International Corporation Adaptive entropy coding in adaptive quantization framework for video signal coding systems and processes
US20020080408A1 (en) * 1999-12-17 2002-06-27 Budge Scott E. Method for image coding by rate-distortion adaptive zerotree-based residual vector quantization and system for effecting same
WO2008063334A2 (en) * 2006-11-09 2008-05-29 Calista Technologies System and method for effectively encoding and decoding electronic information
US20090110066A1 (en) * 2007-10-30 2009-04-30 General Instrument Corporation Method and Apparatus for Selecting a Coding Mode

Family Cites Families (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5933542A (en) * 1996-04-24 1999-08-03 Sony Corporation Method and apparatus for blocking effect reduction in images by post-processing in the spatial domain
KR100269125B1 (en) * 1997-10-25 2000-10-16 윤덕용 Image post processing method and apparatus for reducing quantization effect
US6252994B1 (en) * 1998-01-26 2001-06-26 Xerox Corporation Adaptive quantization compatible with the JPEG baseline sequential mode
US6801665B1 (en) * 1998-09-15 2004-10-05 University Of Maryland Method and apparatus for compressing and decompressing images
US6748362B1 (en) 1999-09-03 2004-06-08 Thomas W. Meyer Process, system, and apparatus for embedding data in compressed audio, image video and other media files and the like
CA2400947A1 (en) 2000-03-06 2001-09-13 Thomas W. Meyer Data embedding in digital telephone signals
US7206459B2 (en) * 2001-07-31 2007-04-17 Ricoh Co., Ltd. Enhancement of compressed images
CN101448162B (en) * 2001-12-17 2013-01-02 微软公司 Method for processing video image
JP4859368B2 (en) * 2002-09-17 2012-01-25 ウラディミール・ツェペルコヴィッツ High-speed codec with minimum required resources providing a high compression ratio
US7890335B2 (en) * 2002-10-10 2011-02-15 Texas Instruments Incorporated Sharing wavelet domain components among encoded signals
KR100813258B1 (en) * 2005-07-12 2008-03-13 삼성전자주식회사 Apparatus and method for encoding and decoding of image data
KR20100046284A (en) * 2005-12-09 2010-05-06 엔비디아 코포레이션 A hardware multi-standard video decoder device
CN101026761B (en) * 2006-02-17 2010-05-12 中国科学院自动化研究所 A Motion Estimation Method for Fast Varying Size Block Matching with Minimal Error
US8059721B2 (en) * 2006-04-07 2011-11-15 Microsoft Corporation Estimating sample-domain distortion in the transform domain with rounding compensation
CN101491102B (en) * 2006-07-20 2011-06-08 高通股份有限公司 Video coding considering postprocessing to be performed in the decoder
EP1883067A1 (en) * 2006-07-24 2008-01-30 Deutsche Thomson-Brandt Gmbh Method and apparatus for lossless encoding of a source signal, using a lossy encoded data stream and a lossless extension data stream
US20080225947A1 (en) * 2007-03-13 2008-09-18 Matthias Narroschke Quantization for hybrid video coding
WO2008122913A2 (en) * 2007-04-04 2008-10-16 Nxp B.V. Decoder for selectively decoding predetermined data units from a coded bit stream
US8442337B2 (en) * 2007-04-18 2013-05-14 Microsoft Corporation Encoding adjustments for animation content
EP2046045A1 (en) 2007-10-02 2009-04-08 Thomson Licensing Methods of encoding and reconstructing image data and devices implementing said methods
US8553994B2 (en) 2008-02-05 2013-10-08 Futurewei Technologies, Inc. Compressive sampling for multimedia coding
US8805106B2 (en) 2008-09-26 2014-08-12 Futurewei Technologies, Inc. System and method for compressing and decompressing images and video
CN101420614B (en) * 2008-11-28 2010-08-18 同济大学 Image compression method and device integrating hybrid coding and wordbook coding
US8340448B2 (en) * 2009-10-06 2012-12-25 Cisco Technology, Inc. Locally variable quantization and hybrid variable length coding for image and video compression
CN101854548B (en) * 2010-05-25 2011-09-07 南京邮电大学 Wireless multimedia sensor network-oriented video compression method
CN101888545B (en) * 2010-05-27 2013-01-02 北京华信新媒技术有限公司 Compression coding method for signal source with low code rate

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5321522A (en) * 1993-07-19 1994-06-14 Xerox Corporation ADCT compression with minimum compression ratio
WO1999029115A1 (en) * 1997-12-01 1999-06-10 Rockwell International Corporation Adaptive entropy coding in adaptive quantization framework for video signal coding systems and processes
US20020080408A1 (en) * 1999-12-17 2002-06-27 Budge Scott E. Method for image coding by rate-distortion adaptive zerotree-based residual vector quantization and system for effecting same
WO2008063334A2 (en) * 2006-11-09 2008-05-29 Calista Technologies System and method for effectively encoding and decoding electronic information
US20090110066A1 (en) * 2007-10-30 2009-04-30 General Instrument Corporation Method and Apparatus for Selecting a Coding Mode

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
See also references of EP2700234A4
T.T.DO; X. LU; J. SOLE: "Compressive sensing with adaptive pixel domain reconstruction for block-based video coding", PROCEEDINGS OF ICIP, 26 September 2010 (2010-09-26), pages 3377 - 3380, XP031814139

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9491475B2 (en) 2012-03-29 2016-11-08 Magnum Semiconductor, Inc. Apparatuses and methods for providing quantized coefficients for video encoding
WO2014078985A1 (en) * 2012-11-20 2014-05-30 Thomson Licensing Method and apparatus for image regularization
WO2014149818A1 (en) * 2013-03-15 2014-09-25 Magnum Semiconductor, Inc. Apparatuses and methods for providing quantized coefficients for video encoding
US9392286B2 (en) 2013-03-15 2016-07-12 Magnum Semiconductor, Inc. Apparatuses and methods for providing quantized coefficients for video encoding
US9794575B2 (en) 2013-12-18 2017-10-17 Magnum Semiconductor, Inc. Apparatuses and methods for optimizing rate-distortion costs in video encoding
CN110262819A (en) * 2019-06-04 2019-09-20 深圳前海微众银行股份有限公司 A kind of the model parameter update method and device of federal study
CN110262819B (en) * 2019-06-04 2021-02-26 深圳前海微众银行股份有限公司 A method and device for updating model parameters of federated learning

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