2014 Data Compression Conference
Linear Rate Estimation Model for HEVC RDO Using Binary Classification Based Regression Sanchuan Guo∗ , Zhenyu Liu∗ , Dongsheng Wang∗ , Qingrui Han+ and Yang Song+ ∗
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TNList, Tsinghua University, Beijing 100084, China {
[email protected]} Huawei Technologies Co., Ltd., Shenzhen 518129, China {
[email protected]}
Rate-Distortion Optimization (RDO) in High Efficiency Video Coding (HEVC) promotes the coding efficiency, but also imposes intensive computation to the encoder, because the complex Syntax-based context-adaptive Binary Arithmetic Coding (SBAC) is performed for each candidate coding configuration to calculate the rate cost. We develop the classification based linear regression method to derive the fast estimation model of rate cost. From each quantization 4 × 4 coefficient block, a characteristic vector which consists of parameters shown in Table 1 is extracted, and the relationship between rate cost and characteristic vector is explored through linear regression. To further improve the accuracy, the characteristic vectors are categorized with the binary classification, which is shown in Fig.1. For the Golomb coding which is wildly used in HEVC, we devise the fast method to estimate its rate cost. In addition, we find that the distribution of each bin value of side information syntax elements is highly correlated with the quantization parameter (QP ), so the linear estimation model of side information can be built with QP as parameters. Experiments demonstrate that the PSNR loss of our methods is averagely 0.0428dB over the baseline, with 28.4% rate computation reduction. Table 1: Feature Parameters for Linear Rate Model ηnz ηz ηlz ηzb ηo ηg1
The number of nonzero coefficients in the block. The number of zeros after the first nonzero coefficient. The leading zero number before the first nonzero coefficient. The number of zero blocks. A serial of consequent zeros is called a zero block. The number of one in first 8 nonzero coefficients. The number of greater than one coefficients in the first 8 nonzero coefficients. Ș Ș Ș
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Fig. 1: Quantization coefficient block classifications This work is supported by Huawei Technologies, Nature Science Foundation of China under Grant No.61373025, and National 863 High-Tech Program of China under Grant No.2012AA010905. 1068-0314/14 $31.00 © 2014 IEEE DOI 10.1109/DCC.2014.17
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