WO2007011160A1 - Dispositif et procede de quantification integree permettant d'obtenir un meilleur rapport signal-bruit - Google Patents

Dispositif et procede de quantification integree permettant d'obtenir un meilleur rapport signal-bruit Download PDF

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Publication number
WO2007011160A1
WO2007011160A1 PCT/KR2006/002829 KR2006002829W WO2007011160A1 WO 2007011160 A1 WO2007011160 A1 WO 2007011160A1 KR 2006002829 W KR2006002829 W KR 2006002829W WO 2007011160 A1 WO2007011160 A1 WO 2007011160A1
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value
dct coefficients
quantization
dct
reference value
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PCT/KR2006/002829
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English (en)
Inventor
Hae-Chul Choi
Jae-Gon Kim
Jin-Woo Hong
Sung-Je Ko
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Electronics And Telecommunications Research Institute
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Priority claimed from KR1020060067093A external-priority patent/KR100785855B1/ko
Application filed by Electronics And Telecommunications Research Institute filed Critical Electronics And Telecommunications Research Institute
Priority to US11/996,099 priority Critical patent/US8428380B2/en
Publication of WO2007011160A1 publication Critical patent/WO2007011160A1/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
    • H04N19/30—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
    • H04N19/36—Scalability techniques involving formatting the layers as a function of picture distortion after decoding, e.g. signal-to-noise [SNR] scalability
    • 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
    • H04N19/10—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
    • H04N19/136—Incoming video signal characteristics or properties
    • 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
    • H04N19/189—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding
    • H04N19/19—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding using optimisation based on Lagrange multipliers

Definitions

  • the present invention relates to a quantization apparatus and method for providi ng improved Signal-to-Noise Ratio (SNR) scalability.
  • SNR Signal-to-Noise Ratio
  • a conventional H.264-based quantization method assigns Discrete Cosine Trans form (DCT) coefficients to quantization intervals according to quantization parameters s et for respective frames.
  • DCT Discrete Cosine Trans form
  • the present invention provides a quantization apparatus and method which obtai n a distribution of Discrete Cosine Transform (DCT) significant coefficients of the residu es of each SNR enhancement layer generated by a video encoder with improved pictur e-quality scalability, and assign DCT coefficients of the corresponding frame to an opti mal quantization interval, using Rate-Distortion (R-D) optimization, thereby providing hig h coding efficiency.
  • DCT Discrete Cosine Transform
  • a quantization apparatus providing improved Signal-to-Noise Ratio (SNR) scalability, including: an R-D optimization unit performing Rate-Distortion (R-D) optimization based on a distribution of Discrete Cosine Transform (DCT) coefficients of each slice and calculating a first ref erence value and a second reference value respectively indicating a start point and an end point of DCT coefficients quantized to "0"; a quantization interval setting unit setting adaptive quantization intervals on the basis of a minimum value and a maximum value of the DCT coefficients, the first reference value, and the second reference value; and a mapping unit mapping the DCT coefficients to the adaptive quantization intervals.
  • R-D Rate-Distortion
  • an encod er providing SNR scalability, including: a quantization unit performing R-D optimization based on a distribution of DCT coefficients of each slice, calculating quantization coeffic ient values and reference values respectively indicating a start value and an end value of DCT coefficients quantized to "0", and performing quantization; and a dequantization unit performing dequantization based on average values of DCT coefficients of respecti ve intervals divided according to the reference values and the quantization coefficient v alues.
  • a codec p roviding improved SNR scalability including: an R-D optimization unit performing R-D o ptimization based on a distribution of DCT coefficients of each slice and calculating a fir st reference value and a second reference value respectively indicating a start point an d an end point of DCT coefficients quantized to "0"; a quantization interval setting unit s etting adaptive quantization intervals based on a minimum value and a maximum value of the DCT coefficients, the first reference value, and the second reference value; a ma pping unit mapping the DCT coefficients to the adaptive quantization intervals; an entro py encoding unit adding, to a bit stream, values encoded based on average values of D CT coefficients of respective intervals divided according to the reference values; and a dequantization unit performing dequantization based on both the average value of the DCT coefficients and quantization coefficient values extracted from the bit stream.
  • a quantiz ation method providing improved SNR scalability including: performing R-D optimization based on a distribution of DCT coefficients of each slice and calculating a first referenc e value and a second reference value respectively indicating a start point and an end p oint of DCT coefficients quantized to "0"; setting adaptive quantization intervals on the b asis of a minimum value and a maximum value of the DCT coefficients, the first referen ce value, and the second reference value; and mapping the DCT coefficients to the ada ptive quantization intervals.
  • a coding method of providing improved SNR scalability including: performing quantization after c alculating by performing R-D optimization on the basis of a distribution of DCT coefficie nts of each slice, calculating quantization coefficient values and reference values respe ctively indicating a start point and an end point of a range of DCT coefficients quantized to "0"; and performing dequantization on the basis of average values of DCT coefficien ts of each section divided based on the reference values and the quantization coefficien t values.
  • a comput er readable recording medium having embodied thereon a computer program for execu ting the method.
  • FIG. 1 illustrates a hierarchical structure for providing picture-quality scalability in a video encoding method supporting picture-quality scalability
  • FIG. 2 is a view for explaining cases in which a rounding artifact is generated in a JSVM progressive quantization method
  • FIG. 3 is a view for more explaining a rounding artifact effect in more detail
  • FIG. 4 is a view for explaining non-significant quantization based on the JSVM 1. 0 standard
  • FIG. 5 is a view for explaining significant quantization based on the JSVM 1.0 sta ndard
  • FIG. 6 is a block diagram of a quantization apparatus for providing improved SN R scalability, according to an embodiment of the present invention
  • FIG. 7 is a graph showing a distribution of Discrete Cosine Transform (DCT) coef ficients
  • FIG. 8 is a graph used to calculate reference values, according to an embodimen t of the present invention.
  • FIG. 9 is a view for explaining a process in which quantization intervals and recon struction values are calculated through Rate-Distortion (R-D) optimization from a distrib ution of DCT coefficients, according to an embodiment of the present invention
  • FIG. 10 is a block diagram of an encoder including a quantization unit for providin g improved SNR scalability, according to an embodiment of the present invention
  • FIG. 11 is a flowchart illustrating a quantization method, according to an embodi ment of the present invention.
  • FIG. 12 is a flowchart illustrating a quantization and dequantization method, acco rding to an embodiment of the present invention.
  • FIGS. 13 through 19 are graphs showing effects obtained by the methods accord ing to the present invention.
  • FIG. 1 is a block diagram illustrating a hierarchical structure for providing picture- quality scalability in a video encoding method supporting picture-quality scalability.
  • a transformed image or an original image is input through the process of transformation, scaling, and quantization, and is generated as an encoded stream in a Signal-to-Noise Ratio (SNR) base layer.
  • SNR Signal-to-Noise Ratio
  • the encoded stream of the SNR base layer is process ed by dequantization, descaling, inverse-transformation, and dequantization and thus is reconstructed as a low and a high image.
  • a difference between the reconstructed im age and the original image is generated as an input image of a SNR enhancement laye r.
  • An enhancement encoding stream is generated in each enhancement layer, thro ugh the same method as that used in the SNR base layer, and transferred to a decoder .
  • a quantization parameter used in each layer is a value obtained by subtr acting 6 from a quantization parameter used in the lower layer.
  • SNR scalability is provided by iterative quantization of the residu al signals computed between the original subband pictures and the reconstructed subb and pictures obtained after decoding the SNR base layer and previous SNR enhancem ent layers.
  • Equation 1 Zy denotes a quantized coefficient, Wy denotes a DCT-transforme d result, MF denotes a multiplication factor, f denotes a rounding offset, and » denotes a right binary shift.
  • f is 2 qblt /3 with resp ect to an intra block, and 2 qblt /6 with respect to an inter block.
  • dequantizatio n is performed using the following Equation 2.
  • Equation 2 can be applied to an image encoding method supporting picture-quality scalabilit y.
  • non-integer numbe rs are rounded to the nearest integer.
  • it is impossible to extract the original non- integer number from the rounded integer is impossible, which causes an irreversible los s.
  • the quantization and dequantization using Equations 1 and 2 do not folio w a distribution of Discrete Cosine Transform (DCT) coefficients of each slice, and cann ot obtain optimal quantization intervals and reconstruction values. Accordingly, encodi ng efficiency is low.
  • DCT Discrete Cosine Transform
  • FIG. 2 is a diagram for explaining cases in which a rounding artifact is generated in a JSVM progressive quantization method. As illustrated in FIG. 2, in the JSVM progressive quantization method, due to the rounding artifact, quantization intervals are not perfectly embedded.
  • FIG. 2 illustrates a case where a quantization interval of a SNR enhanceme nt layer is perfectly embedded in a quantization interval of a SNR base layer.
  • Meanwh ile, (b) and (c) in FIG. 2 illustrate cases where there is a difference between a quantizati on interval of a SNR base layer and a quantization interval of a SNR enhancement laye r due to a rounding artifact. That is, the difference between the coefficients of input re sidues of a current layer and dequantized coefficients of a current layer and the coeffici ents of input residues of the next enhancement layer is likely not to be coherent.
  • a part of values encoded to "1" in a SNR base layer can be mapped to "-1" in a SNR enhancement layer, due to the rounding artifact effect. Also, a part of values encoded to "-1" in the SNR base layer can be mapped to "1" in the SNR enhancement layer, due to the round artifact effect.
  • FIG. 4 is a diagram for explaining non-significant quantization based on the conv entional JSVM 1.0 standard.
  • a case where a quantized DCT coefficient in a base layer is "0" is called “non-sig nificant”.
  • the DCT coefficient of the corresponding enhancement layer mu st be located in an area 411 illustrated in FIG. 4.
  • the DCT coefficient of the enhancement layer may be found in areas 412 and 413 as shown in FIG. 4. Also, due to the rounding artifact, it is difficult to correctly estimate intervals in which quantized DCT coefficients are located (420, 430).
  • FIG. 5 is a view for explaining significant quantization based on the conventional JSVM 1.0 standard.
  • a case where a quantized DCT coefficient in a base layer is 1 is called "significan t".
  • the DCT coefficient of the corresponding enhancement layer must be I ocated in an area 511 illustrated FIG. 5.
  • w hen quantization or dequantization is performed using Equations 1 and 2
  • the DCT coef ficient of the enhancement layer may be found in areas 512 and 513 as illustrated in Fl G. 5.
  • intervals in which quantized DCT coefficients a re located become narrower (520) or wider (530).
  • FIG. 6 is a block diagram of a quantization apparatus 600 for providing improved SNR scalability, according to an embodiment of the present invention.
  • the quantization apparatus 600 includes an R-D optimization unit 610, a quanti zation interval setting unit 620, and a mapping unit 630.
  • the R-D optimization unit 610 performs R-D optimization on the basis of a distrib ution of DCT coefficients of each slice and calculates a first reference value and a seco nd reference value respectively indicating a start point and an end point of a range of D CT coefficients quantized to "0".
  • D denotes an average distortion value
  • R denotes an average bit rate
  • ⁇ denotes a Lagrang e multiplier.
  • N denotes the total number of Wy
  • n ⁇ ⁇ denotes the number of Wy in a ra nge [ ⁇ k> ⁇ k+ i]-
  • the quantization interval setting unit 620 sets adaptive quantization intervals, on the basis of minimum and maximum values of the DCT coefficients and the first and se cond reference values calculated by the R-D optimization unit 610.
  • the minimum value of the DCT coefficients is cto
  • the maximum value of the DCT coefficient s is c( 3 )
  • the first reference value is ⁇ -i
  • the second reference value is ⁇ 2
  • the adaptive quantization interval can be obtained as illustrated in (b) of FIG. 9.
  • the mapping unit 630 maps the DCT coefficients to the adaptive quantization int ervals, thereby performing quantization. In detail, coefficients from the minimum value do of the DCT coefficients to the first reference value cti are mapped to "-1", coefficient s from the second reference value ⁇ 2 to the maximum value ⁇ 3 of the DCT coefficients are mapped to "1", and the remaining coefficients are mapped to "0".
  • FIG. 7 is a graph showing a distribution of the DCT coefficients Wy.
  • an x axis corresponds to values of the DCT coefficients Wy
  • a y axi s corresponds to the number of the DCT coefficients Wy.
  • FIG. 8 is a graph used to calculate reference values, according to an embodime nt of the present invention.
  • RA vc means entropy obtained by reconstruction values and step sizes of a conventional q uantization method.
  • the average distortion value D and the average bit rate R are obtained by varyin g the first reference value ⁇ i from 0 to the minimum value ⁇ o of the DCT coefficients an d varying the second reference value ⁇ 2 from 0 to the maximum value ⁇ 3 of the DCT co efficients.
  • FIG. 8 shows R and D values obtained according to the minimum value ⁇ o of the DCT coefficients and the first reference value ⁇ -i.
  • values which minimize the cost function value J are connected by a solid line.
  • a point which satisfies R ⁇ RAVC of the values is a point 810. Accordingly, the first reference value ⁇ i and the minimum v alue ⁇ 0 of DCT coefficients indicate R and D values corresponding to the point 810.
  • a conventional JSVM quantizer is manufactured un der an assumption that DCT coefficients exist in an area 910.
  • DCT coefficients of SNR enhancement layers can exist only in an area 920.
  • the present invention provides a quantization apparatus, which is ca pable of adaptively setting quantization intervals, considering only parts in which DCT c oefficients of SNR enhancement layers actually exist, thereby achieving higher encodin g efficiency than the conventional JSVM quantization apparatus.
  • FIG. 10 is a block diagram of an encoder 1000 including a quantization unit for pr oviding improved SNR scalability, according to an embodiment of the present invention.
  • the encoder 1000 includes a quantization unit 1010, a dequantization unit 1020, and an entropy coding unit 1030.
  • the quantization unit 1010 performs R-D optimization based on a distribution of DCT coefficients of each slice, calculates quantization coefficient values and reference values indicating a start value and an end value of DCT coefficients quantized to "0", an d performs quantization.
  • the function and technical concept of the quantization unit 1010 is th e same as that of the quantization apparatus 600 described above with reference to Fl G. 6, and therefore, a detailed description thereof will be omitted.
  • the dequantization unit 1020 performs dequantization using Equation 3, on the b asis of values Zy encoded by the quantization unit 1010 and average values ⁇ o, ⁇ -i, and ⁇ 2 of DCT coefficients of respective intervals, wherein ⁇ 0 is an average value of DCT co efficients in the interval [ ⁇ 0 , ⁇ -i], ⁇ i is an average value of DCT coefficients in the interva I [ ⁇ -i, ⁇ 2 ], and ⁇ 2 is an average value of DCT coefficients in the interval [ ⁇ 2 , 0: 3 ].
  • Wi/ (Z ⁇ +Y k )V ij 2 floor(QP/6) ' (3)
  • a codec (not illustrated) for providing improved SNR scalability using the quantiz ation method according to the present invention, includes an encoder and a decoder.
  • the encoder includes an R-D optimization unit, a quantization interval setting unit, a ma pping unit, and an entropy encoder.
  • the decoder includes a dequantization unit.
  • the entropy encoder adds, to a bit stream, compressed values
  • the dequantization unit performs dequantization based on the qu antization coefficients Zy and the DCT coefficient average values ⁇ o, ⁇ i, and 6 2 extracte d from the bit stream.
  • FIG. 11 is a flowchart illustrating a quantization method, according to an embodi ment of the present invention.
  • a distribution of DCT coefficients of each slice rece ived through the quantization unit 1010 of the encoder 1000 is obtained (operation S11 10). Then, R-D optimization is performed based on the obtained distribution of the DC T coefficients, so that a first reference value ⁇ i and a second reference value ⁇ 2 indicati ng a start value and an end value of a range of DCT coefficients quantized to "0" are cal culated (operation S1120).
  • quantization interval setting for setting adaptive quantization intervals base d on a minimum value Wj j _ m in and a maximum value Wjj_ max of the DCT coefficients, the first reference value ⁇ -i, and the second reference value 0 2 is performed (operation S11 30).
  • mapping for mapping the DCT coefficients to the adaptive quantizatio n intervals is performed (operation S1140).
  • coefficients from the minimum value Wi j _ min of the DCT coefficients to the first reference value ⁇ i are mappe d to "-1"
  • coefficients from the second reference value ⁇ 2 to the maximum value Wjj_ ma ⁇ of the DCT coefficients are mapped to "1”
  • the remaining coefficients are mapped t 0 "0".
  • FIG. 12 is a flowchart illustrating a quantization and dequantization method, acco rding to an embodiment of the present invention.
  • a coding method for providing SNR scalability includes quantization by an encod er and dequantization by a decoder. First, R-D optimization is performed based on a distribution of DCT coefficients of each slice, quantization coefficient values and reference values indicating a start value and an end value of a range of DCT coefficients quantized to "0" are calculated, and th en quantization is performed.
  • R-D optimization is performed based on a distribution of DCT coef ficients of each slice, R-D optimization (operation S1220) for calculating a first referenc e value and a second reference value indicating a start value and an end value of a ran ge of DCT coefficients to be quantized to "0" is performed, and then quantization interva
  • I setting for setting adaptive quantization intervals on the basis of the maximum and minimum values of the DCT coefficients and the first and second refere nee values calculated in the operation S1220 is performed.
  • Coefficients from the minimum value of the DCT coefficients to the first reference value are mapped to "-1", coefficients from the second reference value to the maximu m value of the DCT coefficients are mapped to "1”, and the remaining coefficients are mapped to "0", thereby performing quantization (operation S1240).
  • entropy encoding for adding to a bit stream values encoded on the basis of average values of DCT coefficients of respective intervals divi ded according to the reference values, is performed, and the bit stream is transferred to the decoder.
  • FIGS. 13 through 19 are graphs illustrating results obtained using the methods a ccording to the present invention.
  • FIGS. 13 through 19 show results when a FGS layer is stacked on the correspon ding layer in image format. Frame rate conditions are denoted above each graph.
  • left and lower points are rate distortion points of a base I ayer
  • right and upper points are rate distortion points of a first FGS layer.
  • the proposed method has characteristics almost identica I to the conventional method. However, in FIGS. 13, 14, and 15, the proposed method has performance improved by about 0.1 dB, by about 1 dB, and by about 0.8 dB, resp ectively, compared to the conventional method.
  • the present invention can also be embodied as computer readable codes on a c omputer readable recording medium.
  • the computer readable recording medium is an y data storage device that can store data which can be thereafter read by a computer s ystem.
  • Examples of the computer readable recording medium include read-only mem ory (ROM), random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, 0 ptical data storage devices, and carrier waves (such as data transmission through the I nternet).
  • ROM read-only mem ory
  • RAM random-access memory
  • CD-ROMs compact disc-read only memory
  • magnetic tapes magnetic tapes
  • floppy disks 0 ptical data storage devices
  • carrier waves such as data transmission through the I nternet
  • carrier waves such as data transmission through the I nternet.
  • the computer readable recording medium can also be distributed over netwo rk coupled computer systems so that the computer readable code is stored and execute d in a distributed fashion.

Abstract

Procédé et dispositif permettant d'accroître l'efficacité du codage. Pour le codage et le décodage, on calcule de manière optimale des intervalles de quantification et des valeurs de reconstruction via une distribution des coefficients de transformées en cosinus discrètes (DCT) de chaque séquence lorsque les coefficients DCT de chaque couche d'amélioration de rapport signal-bruit sont quantifiés dans un codage vidéo redimensionnable. Le dispositif de codage se compose comme suit: unité d'optimisation du taux de distorsion (RD) à partir d'une distribution des coefficients de transformées en cosinus discrètes de chaque tranche et du calcul d'une première valeur de référence et d'une seconde valeur de référence indiquant respectivement un point de départ et un point final des coefficients DCT quantifiés à '0'; unité de fixation d'intervalles de quantification fixant de tels intervalles adaptatifs à partir d'une valeur minimum et d'une valeur maximum des coefficients DCT, de la première valeur de référence et de la seconde valeur de référence; et unité de mappage reportant les coefficients DCT sur les intervalles de quantification adaptatifs.
PCT/KR2006/002829 2005-07-19 2006-07-19 Dispositif et procede de quantification integree permettant d'obtenir un meilleur rapport signal-bruit WO2007011160A1 (fr)

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KR1020060067093A KR100785855B1 (ko) 2005-07-19 2006-07-18 향상된 ｓｎｒ 스케일러빌리티 제공을 위한 양자화 장치 및방법

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WO2008147152A3 (fr) * 2007-05-31 2009-01-22 Korea Electronics Telecomm Procédé et appareil d'émission de signaux de diffusion numérique, et procédé et appareil de réception de signaux de diffusion numérique
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US10735734B2 (en) 2014-07-28 2020-08-04 Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. Source coding scheme using entropy coding to code a quantized signal
CN107079152B (zh) * 2014-07-28 2021-04-02 弗劳恩霍夫应用研究促进协会 编码器、解码器、用于编码及解码的系统及方法
CN112954323A (zh) * 2014-07-28 2021-06-11 弗劳恩霍夫应用研究促进协会 编码器、解码器、用于编码及解码的系统及方法

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