Browsing by Author "Zhu, Ce"

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    Fast Coding Mode Prediction for Intra Prediction in VVC SCC
    (2024 IEEE International Conference on Image Processing (ICIP 2024), 2024-06-06) Wang, Dayong; Yu, Junyi; Lu, Xin; Dufaux, Frederic; Guo, Hongwei; Guo, Hui; Zhu, Ce
    Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by 34.95% on average while maintaining almost the same coding efficiency.
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    FAST LEARNING-BASED SPLIT TYPE PREDICTION ALGORITHM FOR VVC
    (IEEE, 2023-10) Wang, Dayong; Chen, Liulin; Lu, Xin; Dufaux, Frederic; Li, Weisheng; Zhu, Ce
    As the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding com- plexity, which seriously hinders its widespread application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split type (ST) prediction algorithm for VVC using a deep learning approach. We first construct a large-scale database containing sufficient STs with diverse video resolution and content. Next, since the ST distributions of coding units (CUs) of different sizes are significantly distinct, so we separately design neural net- works for all different CU sizes. Then, we merge ambiguous STs into four merged classes (MCs) to train models to obtain probabilities of MCs and skip unlikely ones. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 67.53% with 1.89% increase in Bjøntegaard delta bit-rate (BDBR) on average.
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    Fast Mode and CU Splitting Decision for Intra Prediction in VVC SCC
    (IEEE, 2024-04-17) Wang, Dayong; Yu, Junyi; Lu, Xin; Dufaux, Frederic; Hang, Bo; Guo, Hui; Zhu, Ce
    Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs a quad-tree plus multi-type tree (QTMT) coding structure for Coding Unit (CU) partitioning and screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse- Code Modulation (BDPCM) early termination and CU splitting early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm improves coding speed on average by 41.14%, with the BDBR increasing by 1.17%.
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    Hybrid strategies for efficient intra prediction in spatial SHVC
    (IEEE, 2022-11-28) Wang, Dayong; Sun, Yu; Lu, Xin; Li, Weisheng; Lele, Xie; Zhu, Ce
    With multi-layer encoding and Inter-layer prediction, Spatial Scalable High Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to speed up its coding to promote widespread and cost-effective SSHVC applications. Specifically, we first reveal that the average RD cost of Inter-layer Reference (ILR) mode is different from that of Intra mode, but they both follow the Gaussian distribution. Based on this discovery, we apply the classic Gaussian Mixture Model and Expectation Maximization to determine whether ILR mode is the best mode thus skipping Intra mode. Second, when coding units (CUs) in enhancement layer use Intra mode, it indicates very simple texture is presented. We investigate their Directional Mode (DM) distribution, and divide all DMs into three classes, and then develop different methods with respect to classes to progressively predict the best DMs. Third, by jointly considering rate distortion costs, residual coefficients and neighboring CUs, we propose to employ the Conditional Random Fields model to early terminate depth selection. Experimental results demonstrate that the proposed algorithm can significantly improve coding speed with negligible coding efficiency losses.
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    Learning-Based Fast Splitting and Directional Mode Decision for VVC Intra Prediction
    (IEEE, 2024-02-19) Huang, Yuanyuan; Yu, Junyi; Wang, Dayong; Lu, Xin; Dufaux, Frederic; Guo, Hui; Zhu, Ce
    As the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding complexity, which seriously hinders its practical application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split mode (SM) and directional mode (DM) decision algorithm for VVC intra prediction using a deep learning approach. Specifically, given the observation that the SM distributions of coding units (CUs) of different sizes are significantly distinct, we first design the neural networks separately and train the SM models for all CUs of different sizes to obtain the probability of SMs and skip the unlikely ones. Second, given a similar observation that the DM distributions of CUs of different sizes are distinct, we design neural networks to train the DM models for all CUs of different sizes separately to obtain the probabilities of DMs, and then adaptively select candidate DMs based on probabilities of their located SMs. Third, after an SM is checked, we select its probability, residual coefficients, rate-distortion (RD) cost, etc. as features, and design a lightweight neural network (LNN) model to early terminate SM selection. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 70.73% with 2.44% increase in Bjøntegaard delta bitrate (BDBR) on average.
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    A novel mode selection-based fast intra prediction algorithm for spatial SHVC
    (IEEE, 2023-06) Wang, Dayong; Sun, Yu; Li, Weisheng; Xie, Lele; Lu, Xin; Dufaux, Frederic; Zhu, Ce
    Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss.
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    A probability-based all-zero block early termination algorithm for QSHVC
    (IEEE, 2023-04-05) Wang, Dayong; Lu, Xin; Dufaux, Frederic; Wang, Qianmin; Li, Weisheng; Hang, Bo; Zhu, Ce
    To seamlessly adapt to time-varying network bandwidths, Quality Scalable High-Efficiency Video Coding (QSHVC) is developed. However, its coding process is overwhelmingly complex, and this seriously limits its wide applications in realtime environments. Therefore, it is of great significance to study fast coding algorithms for QSHVC. In this paper, we propose a novel probability-based All-Zero Block (AZB) early termination algorithm for QSHVC. We observe that the generated residual coefficients follow the Laplace distribution if a CU is accurately predicted. Based on this observation, we derive the sum of squared differences-based AZB decision condition. Second, the probability of each coding mode and coding depth being chosen as the best ones are combined with AZBs to derive the probability-based early termination condition. The experimental results show that the proposed algorithm can improve the average coding speed by 70.15% with a 0.40% increase in BDBR.
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    A Probability-Based Zero-Block Early Termination Algorithm for QSHVC
    (IEEE, 2023-04-05) Wang, Dayong; Lu, Xin; Sun, Yu; Wang, Qianmin; Li, Weisheng; Dufaux, Frederic; Zhu, Ce
    To seamlessly adapt to time-varying network bandwidths, the Quality Scalable High-Efficiency Video Coding (QSHVC) is developed. However, its coding process is overly complex, and this seriously limits its wide applications in real-time environments. Therefore, it is of great significance to study fast coding algorithms for QSHVC. In this paper, we propose a novel probability-based zero-block early termination algorithm for QSHVC. First, we observed that the generated residual coefficients follow the Laplace distribution if a CU is accurately predicted. According to this observation, we derive the sum of squared differences based All-Zero Block (AZB) decision condition. Second, we develop the Hadamard Transform (HT)- based zero-valued quantized coefficient decision condition to obtain zero-valued quantized coefficients and the corresponding Partial-Zero Block (PZB). Third, the probability of each coding mode and coding depth being chosen as the best ones are combined with both AZBs and PZBs to derive the probability-based early termination condition. The experimental results show that the proposed algorithm can improve the average coding speed by 80.6% with a 0.31% decrease in BDBR.