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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (7): 1285-1293, 1319    DOI: 10.3785/j.issn.1008-973X.2022.07.003
    
Fast stepwise all zero block detection algorithm for H.266/VVC
Wei-hong NIU1(),Xiao-feng HUANG1,3,Wei QI1,Hai-bing YIN1,*(),Cheng-gang YAN2
1. College of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
2. College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
3. Advanced Institute of Information Technology, Peking University, Hangzhou 311215, China
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Abstract  

A fast algorithm for all zero block detection was proposed in order to reduce the computational complexity. A fixed threshold was derived based on the hard decision quantization formula in order to detect genuine all zero blocks. Pseudo all zero blocks were further detected by the adaptive threshold related to the transform block size and quantization parameter (QP). The decision was made based on the fully connected neural network (FCNN) for the remaining blocks by extracting eight features that were closely related to the result of quantization. The experimental results showed that the proposed fast algorithm achieved up to 7.382% and 7.237% coding complexity saving under Low Delay B and Random Access configurations with only 0.458% and 0.575% performance loss on average, respectively.

Key words： all zero block      hard decision quantization      quantization parameter      fully connected neural network (FCNN)     
Received: 07 July 2021      Published: 26 July 2022
CLC:  TP 391  
Fund:  国家自然科学基金资助项目（61901150，61572449，61972123）；浙江省自然科学基金资助项目（LQ19F010011）
Corresponding Authors: Hai-bing YIN     E-mail: wh_niu@hdu.edu.cn;yhb@hdu.edu.cn
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Wei-hong NIU
Xiao-feng HUANG
Wei QI
Hai-bing YIN
Cheng-gang YAN
Cite this article:

Wei-hong NIU,Xiao-feng HUANG,Wei QI,Hai-bing YIN,Cheng-gang YAN. Fast stepwise all zero block detection algorithm for H.266/VVC. Journal of ZheJiang University (Engineering Science), 2022, 56(7): 1285-1293, 1319.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.07.003     OR     https://www.zjujournals.com/eng/Y2022/V56/I7/1285


H.266/VVC分步全零块判决快速算法

为了减少编码计算复杂度，提出分步全零块判决快速算法. 基于硬决策量化公式推导固定阈值，判决出真全零块. 通过与变换块尺寸和量化参数（QP）相关的自适应阈值，判决出伪全零块. 通过提取出8个与量化结果密切相关的特征，基于全连接神经网络（FCNN）对剩余未判决的块进行最后判决. 实验结果表明，提出的分步全零块判决快速算法在Low Delay B和Random Access配置下，在性能平均损失分别仅为0.458%和0.575%的情况下，分别平均减少了7.382%和7.237%的编码复杂度.


关键词： 全零块,  硬决策量化,  量化参数,  全连接神经网络(FCNN) 
Fig.1 Frame diagram of stepwise all zero block detection
Fig.2 Distribution of G-AZB、P-AZB and NAZB
M $ \varepsilon $/% $ \eta $/%
SVM FCNN
64 87.17 95.43 3.535
128 83.79 91.65 3.535
256 82.97 93.28 3.535
512 80.62 93.43 3.535
Tab.1 Comparison of support vector machine and fully connected neural network algorithm
Fig.3 Structure of fully connected neural network
Fig.4 Distribution diagram of pre-quantization coefficients
Fig.5 Mean of pre-quantization coefficients ratio
Fig.6 Position annotation of transformed coefficients
Fig.7 Mean of energy ratio in low frequency domain
Fig.8 Mean value of syntax element (SIG)
量化块 M 系数级特征 TU级特征 上下文级特征
$ {n_0} $ $ {n_1} $ $ \;\beta $ SIG( $ {P_1} $) SIG( $ {P_2} $) SIG( $ {P_3} $) CSBF CBF
AZB 64 0.9258 0.0742 0.1537 0.3725 0.3323 0.3546 0.0789 0.2932
AZB 128 0.9383 0.0617 0.1051 0.3729 0.3516 0.3621 0.0826 0.3922
AZB 256 0.9488 0.0512 0.0677 0.3870 0.3586 0.3783 0.0939 0.4193
AZB 512 0.9530 0.0470 0.0426 0.3946 0.3622 0.3825 0.0948 0.4244
NAZB 64 0.8350 0.1337 0.2734 0.5499 0.4958 0.5371 0.1340 0.6625
NAZB 128 0.8602 0.1141 0.2046 0.5467 0.4944 0.5284 0.1267 0.6523
NAZB 256 0.8696 0.1088 0.1473 0.5334 0.4891 0.5209 0.1235 0.6200
NAZB 512 0.8725 0.1061 0.1006 0.5323 0.4879 0.5155 0.1200 0.5648
Tab.2 Comparison of different features between AZB and NAZB
类别 文献[14]方法 文献[15]方法 文献[16]方法 本文算法
$ \vartriangle {T_{{\text{Enc}}}} $/% BD-Rate/% $ \vartriangle {T_{{\text{Enc}}}} $/% BD-Rate/% $ \vartriangle {T_{{\text{Enc}}}} $/% BD-Rate/% $ \vartriangle {T_{{\text{Enc}}}} $/% BD-Rate/%
B 5.02 1.86 4.76 1.56 5.82 1.45 9.15 0.62
C 4.91 1.75 4.06 1.59 4.37 1.64 7.06 0.51
D 4.82 1.82 4.47 1.62 4.42 1.31 6.91 0.40
E 4.89 1.82 3.64 1.52 4.26 1.33 6.66 0.41
F 4.81 1.80 4.37 1.54 4.91 1.22 7.13 0.35
平均值 4.890 1.810 4.260 1.566 4.756 1.390 7.382 0.458
Tab.3 Coding performance comparison in LDB configuration
类别 文献[14]方法 文献[15]方法 文献[16]方法 本文算法
$ \vartriangle {T_{{\text{Enc}}}} $/% BD-Rate/% $ \vartriangle {T_{{\text{Enc}}}} $/% BD-Rate/% $ \vartriangle {T_{{\text{Enc}}}} $/% BD-Rate/% $ \vartriangle {T_{{\text{Enc}}}} $/% BD-Rate/%
A1 5.92 1.98 6.18 1.34 6.90 1.37 8.90 0.59
A2 5.67 1.82 5.45 1.45 6.21 1.37 9.15 0.53
B 4.48 1.89 4.17 1.53 4.33 1.43 6.54 0.58
C 4.33 1.73 3.93 1.56 4.19 1.75 6.97 0.61
D 4.36 1.82 4.36 1.55 4.09 1.59 6.14 0.52
F 3.13 1.87 2.90 1.53 3.08 1.51 5.72 0.62
平均值 4.648 1.852 4.498 1.493 4.800 1.503 7.237 0.575
Tab.4 Coding performance comparison in RA configuration
类别 文献[14]方法 文献[15]方法 文献[16]方法 本文算法
FNR FPR FNR FPR FNR FPR FNR FPR
B 0.333 0.101 0.081 0.090 0.082 0.076 0.043 0.040
C 0.388 0.111 0.089 0.087 0.102 0.090 0.060 0.051
D 0.426 0.116 0.084 0.092 0.080 0.110 0.040 0.078
E 0.481 0.096 0.098 0.084 0.101 0.109 0.066 0.073
F 0.469 0.099 0.097 0.089 0.087 0.087 0.074 0.061
平均值 0.419 0.105 0.090 0.088 0.090 0.094 0.057 0.061
Tab.5 Detection accuracy comparison in LDB configuration
类别 文献[14]方法 文献[15]方法 文献[16]方法 本文算法
FNR FPR FNR FPR FNR FPR FNR FPR
A1 0.457 0.108 0.093 0.109 0.105 0.094 0.071 0.076
A2 0.355 0.105 0.096 0.110 0.098 0.104 0.074 0.078
B 0.508 0.115 0.107 0.100 0.087 0.089 0.053 0.071
C 0.456 0.124 0.108 0.103 0.086 0.077 0.067 0.052
D 0.536 0.099 0.090 0.105 0.083 0.086 0.048 0.063
F 0.655 0.105 0.088 0.087 0.095 0.097 0.057 0.067
平均值 0.495 0.109 0.097 0.102 0.092 0.091 0.062 0.068
Tab.6 Detection accuracy comparison in RA configuration
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