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A Fast Intra Mode Prediction Decision Algorithm Based on Neighborhood Grouping for HEVC

机译：基于邻域分组的HEVC快速帧内模式预测决策算法

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摘要

High Efficiency Video Coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it requires a very high computational complexity due to its exhaustive calculating process to find the optimal prediction mode for the current CU (Coding Unit). Aiming to solve the problem, a fast intra mode prediction decision algorithm based on neighborhood grouping is presented for HEVC in this article. The contribution of this article lies in the fact that the authors successfully use the neighborhood grouping technology to rapidly find the optimal prediction mode for the current CU, which can significantly reduce computation complexity for HEVC. Specifically, they use the correlative information of adjacent angle prediction mode in their first scheme to quickly reduce the number of Rough Mode Decision (RMD); then they use the correlation between Most Probable Mode (MPM) and the first candidate mode selected as the optimal prediction mode to quickly determine the number of candidate modes; at last, they quickly find the optimal prediction mode with the minimum Rate Distortion Cost (RDCost) by using neighborhood grouping technology or direct calculation approach based on the number of candidate modes. As a result, their proposed algorithm can efficiently solve the problem above. The simulation results show that our proposed intra mode prediction decision algorithm based on neighborhood grouping in this article can reduce about 22.3% computational complexity on average only at a cost of 0.03% bit rate increase and 0.26% PSNR decline compared with the standard reference HM16.1 algorithm. (C) 2019 Society for Imaging Science and Technology.
机译：高效视频编码（HEVC）采用四叉树编码树单元（CTU）结构来提高其编码效率，但同时，由于其详尽的计算过程以找到当前的最佳预测模式，因此需要非常高的计算复杂性CU（编码单位）。为了解决这一问题，本文提出了一种基于邻域分组的HEVC快速帧内模式预测决策算法。本文的贡献在于以下事实：作者成功地使用了邻域分组技术来快速找到当前CU的最佳预测模式，这可以显着降低HEVC的计算复杂性。具体地，他们在他们的第一种方案中使用相邻角度预测模式的相关信息来快速减少粗糙模式决策（RMD）的数量。然后，他们使用最有可能模式（MPM）和被选为最佳预测模式的第一个候选模式之间的相关性来快速确定候选模式的数量；最后，他们利用邻域分组技术或基于候选模式数量的直接计算方法，以最小的速率失真成本（RDCost）快速找到最优的预测模式。结果，他们提出的算法可以有效地解决上述问题。仿真结果表明，本文提出的基于邻域分组的帧内模式预测决策算法与标准参考HM16相比，仅能增加0.03％的比特率和0.26％的PSNR下降，平均可降低22.3％的计算复杂度。 1种算法。 （C）2019影像科学与技术学会。

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  • 来源
    《Journal of Imaging Science and Technology》 |2019年第6期|060503.1-060503.13|共13页
  • 作者

    Wang Jianhua; Xu Fujian; Diao Yanliang; Liu Jun; Lan Yubin; Cheng Lianglun;

  • 作者单位

    South China Agr Univ Coll Elect Engn Guangzhou Peoples R China|Natl Ctr Int Collaborat Res Precis Agr Aviat Pest Guangzhou Peoples R China;

    South China Agr Univ Coll Elect Engn Guangzhou Peoples R China;

    Guangdong Polytech Norma Univ Coll Automat Guangzhou Peoples R China;

    Natl Ctr Int Collaborat Res Precis Agr Aviat Pest Guangzhou Peoples R China;

    Guangdong Univ Technol Fac Automat Guangzhou Peoples R China;

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