Browsing by Author "Sun, Yu"

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    Dynamic niching particle swarm optimization with an external archive-guided mechanism for multimodal multi-objective optimization
    (Elsevier, 2023-10-19) Sun, Yu; Chang, Yuqing; Yang, Shengxiang; Wang, Fuli
    Multimodal multi-objective optimization problems (MMOPs) contain multiple equivalent Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). However, simultaneously locating well-distributed and well-converged multiple equivalent global PSs and PF remains challenging. Therefore, this paper proposes dynamic niching particle swarm optimization (PSO) with an external archive-guided (AG) mechanism, termed DNPSO-AG, for solving MMOPs. In DNPSO-AG, a clustering-based dynamic niching technique is integrated with PSO to divide the population into multiple niches. In addition, a leader updating method controls the updating of the leaders. Furthermore, a novel external archive-guided mechanism guides the evolution of multiple niches and enhances the distribution of solutions, which comprises two strategies: the adaptive division of the external archive strategy, which adaptively divides the external archive into multiple sub-archives, and the distance-based sub-archive and niche matching strategy, which assigns sub-archives to multiple niches for maintenance. The experimental results demonstrate that the proposed DNPSO-AG outperforms seven other state-of-the-art competitors on the CEC 2019 MMOP test suite in terms of the inverted generational distance (IGD) and IGD in the decision space (IGDX) metrics, with improvements of 21.3% and 9.1% over the best-performing competitor, respectively.
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    Gaussian Distribution-Based Mode Selection for Intra Prediction Of Spatial SHVC
    (The 29th IEEE International Conference on Image Processing (IEEE ICIP), 2022-06-20) Wang, Dayong; Wang, Xin; Sun, Yu; Weisheng, Li; Lu, Xin; Dufaux, Frederic
    Due to the diversity of terminal devices, Spatial Scalable High Efficiency Video Coding (SSHVC) is an efficient solution to meet this requirement. However, its coding process is very complex, which seriously prevents its wide applications. Therefore, it is very crucial to reduce coding complexity and improve coding speed. In this paper, we propose a Gaussian Distribution-based Mode Selection for Intra Prediction of SSHVC. We show that the rate distortion costs of Inter-layer Reference (ILR) mode and Intra mode are significantly different, and both follow a Gaussian distribution. Based on this discovery, we propose to use a Bayes decision rule to determine whether ILR is the best mode so as to skip Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve coding speed with negligible coding efficiency losses.
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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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    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 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.