US20130188691A1 - Quantization matrix design for hevc standard - Google Patents

Quantization matrix design for hevc standard Download PDF

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Publication number
US20130188691A1
US20130188691A1 US13/597,131 US201213597131A US2013188691A1 US 20130188691 A1 US20130188691 A1 US 20130188691A1 US 201213597131 A US201213597131 A US 201213597131A US 2013188691 A1 US2013188691 A1 US 2013188691A1
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quantization matrices
intra
matrices
computer
player
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US13/597,131
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English (en)
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Munsi Haque
Ali Tabatabai
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Sony Corp
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Sony Corp
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Priority to US13/597,131 priority Critical patent/US20130188691A1/en
Assigned to SONY CORPORATION reassignment SONY CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HAQUE, MUNSI, TABATABAI, ALI
Priority to JP2013010588A priority patent/JP5633584B2/ja
Priority to EP13150526.5A priority patent/EP2618574B1/fr
Priority to EP14171414.7A priority patent/EP2782346A1/fr
Priority to EP14171373.5A priority patent/EP2779656A1/fr
Priority to CN201310024330XA priority patent/CN103220517A/zh
Priority to BR112014016399A priority patent/BR112014016399A8/pt
Priority to KR1020147019323A priority patent/KR20140101867A/ko
Priority to CA2860072A priority patent/CA2860072A1/fr
Priority to PCT/US2013/022274 priority patent/WO2013109971A1/fr
Publication of US20130188691A1 publication Critical patent/US20130188691A1/en
Priority to JP2014078790A priority patent/JP2014147111A/ja
Priority to US16/902,409 priority patent/US11115662B2/en
Abandoned legal-status Critical Current

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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
    • H04N19/10—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
    • H04N19/126—Details of normalisation or weighting functions, e.g. normalisation matrices or variable uniform quantisers
    • 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
    • H04N19/157—Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
    • H04N19/159—Prediction type, e.g. intra-frame, inter-frame or bidirectional frame prediction
    • 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
    • H04N19/463—Embedding additional information in the video signal during the compression process by compressing encoding parameters before transmission

Definitions

  • the present invention relates to the field of image processing. More specifically, the present invention relates to high efficiency video coding.
  • High Efficiency Video Coding also known as MPEG-H Part 2
  • MPEG-H Part 2 is a draft video compression standard, a successor to H.264/MPEG-4 AVC (Advanced Video Coding), currently under joint development by the ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG).
  • MPEG and VCEG have established a Joint Collaborative Team on Video Coding (JCT-VC) to develop the HEVC standard.
  • JCT-VC Joint Collaborative Team on Video Coding
  • HEVC improves video quality and doubles the data compression ratio compared to H.264, and scales from 320 ⁇ 240 to 7680 ⁇ 4320 pixels resolution.
  • Quantization (scaling) matrices for HEVC standards using an HVS-based mathematical model and data analysis are described herein.
  • a quadratic parameter model-based quantization matrix design is also included.
  • a method of implementing a quantization matrix design for high efficiency video coding programmed in a memory of a device comprises determining intra quantization matrices of square-shaped blocks and converting the intra quantization matrices of the square-shaped blocks into corresponding inter square-shaped quantization matrices.
  • the method further comprises determining intra quantization matrices of rectangular-shaped blocks.
  • the method further comprises converting the intra quantization matrices of the rectangular-shaped blocks into corresponding inter rectangular-shaped quantization matrices. Converting comprises using reference advanced video coding quantization matrices model-based algorithms.
  • the intra quantization matrices are derived from contrast sensitivity functions adjustment-based algorithms.
  • the intra quantization matrices are selected from the group consisting of 4 ⁇ 4, 8 ⁇ 8, 16 ⁇ 16 and 32 ⁇ 32.
  • the intra quantization matrices are selected from the group consisting of 16 ⁇ 4, 32 ⁇ 8, 8 ⁇ 2 and 32 ⁇ 2.
  • the device is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, an portable music player, a tablet computer, a video player, a DVD writer/player, a Blu-ray writer/player, a television and a home entertainment system.
  • a method of implementing a quantization matrix design for high efficiency video coding programmed in a memory of a device comprises determining intra quantization matrices of square-shaped blocks and the intra quantization matrices of rectangular-shaped blocks and converting the intra quantization matrices of the square-shaped blocks into corresponding inter square-shaped quantization matrices and the intra quantization matrices of the rectangular-shaped blocks into corresponding inter rectangular-shaped quantization matrices. Converting comprises using reference advanced video coding quantization matrices model-based algorithms.
  • the intra quantization matrices are derived from contrast sensitivity functions adjustment-based algorithms.
  • the intra quantization matrices are selected from the group consisting of 4 ⁇ 4, 8 ⁇ 8, 16 ⁇ 16 and 32 ⁇ 32.
  • the intra quantization matrices are selected from the group consisting of 16 ⁇ 4, 32 ⁇ 8, 8 ⁇ 2 and 32 ⁇ 2.
  • the device is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, an portable music player, a tablet computer, a video player, a DVD writer/player, a Blu-ray writer/player, a television and a home entertainment system.
  • an apparatus comprises a memory for storing an application, the application for determining intra quantization matrices of square-shaped blocks and converting the intra quantization matrices of the square-shaped blocks into corresponding inter square-shaped quantization matrices and a processing component coupled to the memory, the processing component configured for processing the application.
  • the apparatus further comprises determining intra quantization matrices of rectangular-shaped blocks.
  • the apparatus further comprises converting the intra quantization matrices of the rectangular-shaped blocks into corresponding inter rectangular-shaped quantization matrices. Converting comprises using reference advanced video coding quantization matrices model-based algorithms.
  • the intra quantization matrices are derived from contrast sensitivity functions adjustment-based algorithms.
  • the intra quantization matrices are selected from the group consisting of 4 ⁇ 4, 8 ⁇ 8, 16 ⁇ 16 and 32 ⁇ 32.
  • the intra quantization matrices are selected from the group consisting of 16 ⁇ 4, 32 ⁇ 8, 8 ⁇ 2 and 32 ⁇ 2.
  • the apparatus is selected from the group consisting of a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, an portable music player, a tablet computer, a video player, a DVD writer/player, a Blu-ray writer/player, a television and a home entertainment system.
  • FIG. 1 illustrates Modular Transfer Function (MTF) curves at different peak frequencies according to some embodiments.
  • MTF Modular Transfer Function
  • FIG. 2 illustrates a video codec according to some embodiments.
  • FIG. 3 illustrates comparative Q-matrices (intra 4 ⁇ 4) according to some embodiments.
  • FIG. 4 illustrates comparative Q-matrices (inter 4 ⁇ 4) according to some embodiments.
  • FIG. 5 illustrates comparative Q-matrices (intra 4 ⁇ 4) DCT/DST or DST/DCT according to some embodiments.
  • FIG. 6 illustrates comparative Q-matrices (intra 8 ⁇ 8) according to some embodiments.
  • FIG. 7 illustrates comparative Q-matrices (inter 8 ⁇ 8) according to some embodiments.
  • FIG. 8 illustrates 4 ⁇ 4 HVS Q-matrices generated using quadratic parameters according to some embodiments.
  • FIG. 9 illustrates 4 ⁇ 4 HVS Q-matrices generated using quadratic parameters according to some embodiments.
  • FIG. 10 illustrates intra and inter Q-matrices according to some embodiments.
  • FIG. 11 illustrates intra and inter Q-matrices (16 ⁇ 4) according to some embodiments.
  • FIG. 12 illustrates results for intra quadratic models according to some embodiments.
  • FIG. 13 illustrates results for inter quadratic models according to some embodiments.
  • FIG. 14 illustrates a flowchart of a method of implementing a quantization matrix design according to some embodiments.
  • FIG. 15 illustrates a block diagram of an exemplary computing device configured to implement the quantization matrix design according to some embodiments.
  • the quantization matrix design for HEVC standards includes an HVS-based mathematical model and a quadratic parameter model.
  • Intra Q-matrices of square-shaped blocks or rectangular-shaped blocks for HEVC standards are also included.
  • the intra square-shaped or rectangular-shaped Q-matrices are converted into corresponding inter square-shaped or rectangular-shaped Q-matrices using reference AVC Q-matrices model-based algorithms.
  • intra Q-matrices are derived from the contrast sensitivity functions adjustment-based algorithms.
  • quadratic parameter Q-matrix design a set of quadratic parameters list is derived from referenced input Q-matrices from HVS models or reference AVC Q-matrices model and subsequently used for generating quadratic model based Q-matrices. Then, the intra Q-matrices are converted into corresponding inter Q-matrices using the results derived from the AVC Q-matrix analysis.
  • LUT Look-Up Table
  • the entries of these scaling lists are the zig-zag scanned coefficients of the scaling matrices.
  • the default scaling lists are developed using two example models. The first one is based on the HVS model and the second is designed with quadratic parameter models for symmetric scaling matrices.
  • scaling list tables Another example of storing the scaling list tables is a parametric model where tables are regenerated in the encoder and decoder at the expense of additional computations.
  • the parameter models for scaling matrices are able to be symmetric, multi-parameters (3 to 6) or asymmetric, multi-parameters (2 ⁇ 3 to 6) depending upon the encoded picture/sequences.
  • a symmetric quadratic parameter model for the default scaling list that contains 4 parameters (par_a0, par_b0, par_c0 and par_d0) for a Quadratic equation is shown:
  • f(u,v) is the radial frequency
  • FIG. 1 shows the Modular Transfer Function (MTF) curves at different peak frequencies according to some embodiments.
  • MTF Modular Transfer Function
  • the HVS is modeled as a nonlinear point transformation followed by an MTF, when assuming the HVS is isotropic.
  • the curves are also able to be referred to as Contrast Sensitivity Function (CSF) curves for HVS models.
  • CSF indicates how sensitive people are to the various frequencies of visual stimuli. According to CSF, people are most sensitive to middle frequencies. However, people are very insensitive to the ends of the spectrum frequencies such as very low and very high frequencies.
  • delta_u dot-pitch in width direction (u) for the display terminal
  • Average qp 12 (AVC) or 16 (JPEG, MPEG-2, MPEG-4:2, HEVC).
  • a Spatio-Temporal Just Noticeable Distortion (ST-JND) model is utilized.
  • ST-JND Spatio-Temporal Just Noticeable Distortion
  • CSF Contrast Sensitivity Function
  • a temporal parameter is inserted.
  • AD in video signals depends on both spatial and temporal HVS masking effects. Spatial masking is due to the following aspects: frequency representation type, luminance variations and the presence of patterns such as textured regions that boost masking effects.
  • Temporal masking depends on motion activity present between two successive frames at the distortion is less noticeable in areas with fast movements.
  • the ST-JND model accounts for all of these masking effects.
  • the ST-JND model works in the DCT domain over the luminance component of each video frame, is exploited in both rate allocation and perceptual weighting of the distortion in ME and RD optimization encoding processes, provides a AD threshold for each DCT coefficient, and organizes these threshold in a matrix with the same width and height of the video frame being coded.
  • the ST-JND model is able to utilize spatial masking component modeling, luminance variations masking, image patterns masking and temporal masking.
  • inter Q-matrix weighting is implemented.
  • Three different frequency weighting strategies to modulate HVS-based Q-matrix models are able to be implemented—strategy 0: no frequency weighting, strategy 1: details preserved (more high frequencies) and strategy 2: details blurred (less high frequencies).
  • Neighboring macroblocks (A, B, C, D) contexts (coding type) are used to devise rules for current macroblocks in I/P/B pictures.
  • Strategy 0 skip_mode, Intra — 16 ⁇ 16
  • strategy 1 intra4 ⁇ 4_DC, inter — 4 ⁇ 4, intra_MB in P/B pictures
  • Quadratic Model for Quantization Matrix (Q-Matrix)
  • a symmetric quadratic model for an n ⁇ n Q-matrix is given as:
  • FIG. 2 shows a video codec according to some embodiments.
  • FIG. 3 illustrates comparative Q-matrices (intra 4 ⁇ 4) according to some embodiments. Shown include an HVS-based matrix, an AVC model, an EQM AVC-HR matrix and an AVC “Soft” HVS models matrix.
  • FIG. 4 illustrates comparative Q-matrices (inter 4 ⁇ 4) according to some embodiments. The Q-matrices include a quadratic model matrix, an AVC model matrix, an EQM AVC-HR matrix, a MobilyGen matrix and an AVC “Soft” HVS models matrix.
  • FIG. 5 illustrates comparative Q-matrices (intra 4 ⁇ 4) DCT/DST or DST/DCT according to some embodiments.
  • FIG. 6 illustrates comparative Q-matrices (intra 8 ⁇ 8) according to some embodiments.
  • FIG. 7 illustrates comparative Q-matrices (inter 8 ⁇ 8) according to some embodiments.
  • the Q-matrices include a quadratic model matrix, an AVC model matrix, a MobilyGen Luma matrix, an EQM AVC-HR matrix, and an AVC “Soft” HVS models matrix.
  • 4 ⁇ 4 and 8 ⁇ 8 inter and intra Q-matrices are able to be derived using AVC default matrices as references.
  • 8 ⁇ 8 inter models are able to be used to generate 16 ⁇ 16 and 32 ⁇ 32 inter Q-matrices.
  • AVC default matrix values are able to be used to derive (a, b, c, d) parameters for intra/inter cases
  • FIG. 8 illustrates 4 ⁇ 4 HVS Q-matrices generated using quadratic parameters according to some embodiments.
  • FIG. 9 illustrates 4 ⁇ 4 HVS Q-matrices generated using quadratic parameters according to some embodiments.
  • HVS properties are able to be used to correct shortcomings of mathematical models such as MSE.
  • HVS is less sensitive to details in areas with high amount of texture activities, and more noise is able to be tolerated in regions of a frame which are highly textured. More noise is able to be hidden in darker or brighter areas when compared to the intensity adapted by HVS.
  • HVS acts as a band-pass filter in terms of spatial frequency response. A peak at around 8 cycles per degree of visual angle. More noise is able to be hidden in areas with higher spatial frequencies. Object boundaries are able to be preserved.
  • HVS is very sensitive to unpreserved edges of rigid objects espectially in the presence of motion in video sequences.
  • HVS is able to be used for macroblock classification and perceptual model-based bit-rate control. Macroblocks are able to be classified as textured, dark contrast, smooth, edge, detailed or normal. R-D based qP parameter adjustment (not Q-matrix) is able to be implemented.
  • the input includes HVS intra quantization matrices (QM_hvs_intra) such as 4 ⁇ 4, 8 ⁇ 8, 16 ⁇ 16 or 32 ⁇ 32.
  • AVC matrix models are used to perform the conversion.
  • the output is HVS-based inter quantization matrices (QM_hvs_inter).
  • the intra->inter Q-matrix (square) conversion algorithms include:
  • HVS models are used to generate intra Q-matrices (BLK_X ⁇ BLK_Y) which are able to be 16 ⁇ 4, 32 ⁇ 8, 8 ⁇ 2, 32 ⁇ 2.
  • the y-direction component frequency has the dominant contribution for the Q-matrices (y-frequency grid is larger):
  • FIG. 10 illustrates intra and inter Q-matrices according to some embodiments.
  • FIG. 11 illustrates intra and inter Q-matrices (16 ⁇ 4) according to some embodiments.
  • a Q-matrix (square-shaped, symmetric) is able to be designed by using either of two different models.
  • intra Q-matrices are designed using HVS, and then inter Q-matrices are designed by using intra->inter relationship derived from AVC Q-matrices of 8 ⁇ 8 blocks.
  • Quadratic parameters-based design two types of reference input Q-matrices are used to derive quadratic parameter-sets: HVS model-based Q-matrices and AVC 4 ⁇ 4 and 8 ⁇ 8 Q-matrices, and 16 ⁇ 16 and 32 ⁇ 32 Q-matrices interpolated from 8 ⁇ 8 Q-matrix. Then, quadratic parameter sets are used to generate output Q-matrices.
  • a non-square, Q-Matrix is generated from a square Q-matrix. Selective columns/rows are picked. Both HVS and quadratic parameter-based Q-matrices are used as input Q-matrices.
  • Intra/inter symmetric square and non-square matrices are determined with 2 design models (HVS model-based and AVC and AVC-type). Matrix sizes include: 4 ⁇ 4, 8 ⁇ 8, 16 ⁇ 16, 32 ⁇ 32.
  • FIG. 12 illustrates results for intra quadratic models according to some embodiments.
  • FIG. 13 illustrates results for inter quadratic models according to some embodiments.
  • FIG. 14 illustrates a flowchart of a method of implementing a quantization matrix design according to some embodiments.
  • intra quantization matrices are determined.
  • the intra quantization matrices are converted into corresponding inter quantization matrices.
  • intra quantization matrices are determined for square-shaped blocks and rectangular-shaped blocks.
  • the conversion from intra square-shaped quantization matrices to inter quantization matrices is by using reference AVC Q-matrix model based algorithms.
  • the conversion from intra rectangular-shaped quantization into the corresponding inter rectangular-shaped quantization matrices is by using reference AVC Q-matrix model based algorithms applied to a number of matrix-rows due to repetitive nature of them.
  • contrast sensitivity functions adjustment-based algorithms are used extensively to derive intra quantization matrices.
  • quadratic parameter quantization matrix design a set of quadratic parameters list is generated by using reference input quantization matrices from HVS models or AVC reference quantization matrices as input matrices. Subsequently, these newly derived quadratic parameters are used to generate quadratic model-based quantization matrices by using a quadratic equation. In some embodiments, fewer or more steps are implemented.
  • FIG. 15 illustrates a block diagram of an exemplary computing device 1500 configured to implement the quantization matrix design according to some embodiments.
  • the computing device 1500 is able to be used to acquire, store, compute, process, communicate and/or display information such as images, videos and audio.
  • a computing device 1500 is able to be used to acquire and store a video.
  • the quantization matrix design is typically used during or after acquiring a video.
  • a hardware structure suitable for implementing the computing device 1500 includes a network interface 1502 , a memory 1504 , a processor 1506 , I/O device(s) 1508 , a bus 1510 and a storage device 1512 .
  • the choice of processor is not critical as long as a suitable processor with sufficient speed is chosen.
  • the memory 1504 is able to be any conventional computer memory known in the art.
  • the storage device 1512 is able to include a hard drive, CDROM, CDRW, DVD, DVDRW, Blu-Ray®, flash memory card or any other storage device.
  • the computing device 1500 is able to include one or more network interfaces 1502 .
  • An example of a network interface includes a network card connected to an Ethernet or other type of LAN.
  • the I/O device(s) 1508 are able to include one or more of the following: keyboard, mouse, monitor, display, printer, modem, touchscreen, button interface and other devices.
  • the hardware structure includes multiple processors and other hardware to perform parallel processing.
  • Quantization matrix design application(s) 1530 used to perform quantization matrix design are likely to be stored in the storage device 1512 and memory 1504 and processed as applications are typically processed. More or fewer components shown in FIG. 15 are able to be included in the computing device 1500 .
  • Quantization matrix design hardware 1520 is included.
  • the computing device 1500 in FIG. 15 includes applications 1530 and hardware 1520 for implementing the quantization matrix design, the quantization matrix design is able to be implemented on a computing device in hardware, firmware, software or any combination thereof.
  • the quantization matrix design applications 1530 are programmed in a memory and executed using a processor.
  • the quantization matrix design hardware 1520 is programmed hardware logic including gates specifically designed to implement the method.
  • the quantization matrix design application(s) 1530 include several applications and/or modules. In some embodiments, modules include one or more sub-modules as well.
  • suitable computing devices include a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone (e.g. an iPhone®), a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, a portable music device (e.g. an iPod®), a tablet computer (e.g. an iPad®), a video player, a DVD writer/player, a Blu-ray® writer/player, a television, a home entertainment system or any other suitable computing device.
  • a personal computer e.g. an iPod®
  • a tablet computer e.g. an iPad®
  • video player e.g. an iPod®
  • DVD writer/player e.g. an iPad®
  • Blu-ray® writer/player e.g. an iPad®
  • a device such as a digital camera is able to be used to acquire a video or image.
  • the quantization matrix design is automatically used for performing image/video processing.
  • the quantization matrix design is able to be implemented automatically without user involvement.
  • the quantization matrix design enables faster processing of information and reducing storage space requirements.
  • Potential applications of this implementation include use with the HEVC codec.
  • HVS model-based scaling list matrices performed better than the HM5.0 4 ⁇ 4 and 8 ⁇ 8 default matrices.
  • the default scaling list matrices in HM5.0 include 4 ⁇ 4/8 ⁇ 8 AVC and 16 ⁇ 16/32 ⁇ 32 HVS model-based matrices.
  • the 4 ⁇ 4/8 ⁇ 8 AVC scaling list matrices in HM5.0 are replaced with the corresponding HVS-model based matrices in the Draft International Standard (DIS).
  • the 4 ⁇ 4 and 8 ⁇ 8 HVS model-based matrices are developed using the HVS modeling method as done in the HM5.0 default 16 ⁇ 16 and 32 ⁇ 32 scaling list matrices.

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Application Number Priority Date Filing Date Title
US13/597,131 US20130188691A1 (en) 2012-01-20 2012-08-28 Quantization matrix design for hevc standard
JP2013010588A JP5633584B2 (ja) 2012-01-20 2013-01-04 Ｈｅｖｃ規格のための量子化マトリクス設計
EP13150526.5A EP2618574B1 (fr) 2012-01-20 2013-01-08 Conception d'une matrice de quantification pour standard HEVC
EP14171414.7A EP2782346A1 (fr) 2012-01-20 2013-01-08 Conception d'une matrice de quantification pour standard HEVC
EP14171373.5A EP2779656A1 (fr) 2012-01-20 2013-01-08 Conception d'une matrice de quantification pour standard HEVC
CN201310024330XA CN103220517A (zh) 2012-01-20 2013-01-14 Hevc标准的量化矩阵设计
PCT/US2013/022274 WO2013109971A1 (fr) 2012-01-20 2013-01-18 Conception de matrice de quantification pour norme hevc
BR112014016399A BR112014016399A8 (pt) 2012-01-20 2013-01-18 método de implementação de um desenho de matriz de quantização, e, aparelho
KR1020147019323A KR20140101867A (ko) 2012-01-20 2013-01-18 Hevc 표준용 양자화 행렬 설계
CA2860072A CA2860072A1 (fr) 2012-01-20 2013-01-18 Conception de matrice de quantification pour norme hevc
JP2014078790A JP2014147111A (ja) 2012-01-20 2014-04-07 Ｈｅｖｃ規格のための量子化マトリクス設計
US16/902,409 US11115662B2 (en) 2012-01-20 2020-06-16 Quantization matrix design for HEVC standard

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BR112014016399A2 (pt) 2017-06-13
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