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<p>Since Spatial Scalable High Efficiency Video Coding (SSHVC) utilizes multi-layer encoding and inter-layer prediction, it has extremely high coding complexity which has severely hindered its wide spread. In order to improve coding speed, we develop a fast intra coding algorithm to improve coding speed of SSHVC. First, we utilize the textural feature to obtain the probabilities of using Coding Units (CUs), so as to predict candidate CUs and exclude unlikely ones. Second, we jointly utilize the textural features, the neighboring Coding Modes (CMs) and Rate Distortion (RD) cost to obtain the probability of Intra Mode (IM), which is then combined with the probability of its collocated CU to exclude unlikely IMs. Finally, we investigate distribution of Direction Modes (DMs), which are then combined with the probability of their collocated CU to predict candidate DMs and exclude unlikely ones. Experimental results demonstrate that the proposed algorithm can significantly improve coding speed by 78.64% with negligible coding efficiency change by a 0.27% decrease in BDBR.</p>

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Jointly probability-based fast intra prediction algorithm for spatial SHVC

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月4日

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  • Weian Li,
  • Weihua Ou,
  • Hongbing Wang,
  • Yang Wu,
  • Yunhao Zhong

摘要

Since Spatial Scalable High Efficiency Video Coding (SSHVC) utilizes multi-layer encoding and inter-layer prediction, it has extremely high coding complexity which has severely hindered its wide spread. In order to improve coding speed, we develop a fast intra coding algorithm to improve coding speed of SSHVC. First, we utilize the textural feature to obtain the probabilities of using Coding Units (CUs), so as to predict candidate CUs and exclude unlikely ones. Second, we jointly utilize the textural features, the neighboring Coding Modes (CMs) and Rate Distortion (RD) cost to obtain the probability of Intra Mode (IM), which is then combined with the probability of its collocated CU to exclude unlikely IMs. Finally, we investigate distribution of Direction Modes (DMs), which are then combined with the probability of their collocated CU to predict candidate DMs and exclude unlikely ones. Experimental results demonstrate that the proposed algorithm can significantly improve coding speed by 78.64% with negligible coding efficiency change by a 0.27% decrease in BDBR.