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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.
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Received: 07 July 2021
Published: 26 July 2022
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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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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)
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