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Efficient Multi-Strategy Intra Prediction for Quality Scalable High Efficiency Video Coding

机译:用于质量可扩展高效视频编码的高效多策略帧内预测

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摘要

As an extension of high efficiency video coding (HEVC), the scalable high efficiency video coding (SHVC) introduces multiple layers with inter-layer predictions, which greatly increases the complexity on top of the already complicated HEVC encoder. In intra prediction for quality SHVC, coding tree unit allows recursive splitting into four depth levels, which considers 35 intra prediction modes and inter-layer reference (ILR) mode to determine the best possible mode at each depth level. This achieves the highest coding efficiency but incurs a substantially high computational complexity. In this paper, we propose a novel intra prediction scheme to effectively speed up the enhancement layer intra coding in quality SHVC. The new features of the proposed framework include: first, spatial correlation and its correlation degree are combined to predict most probable depth level candidates. Second, for a given depth candidate, based on the probabilities of the ILR mode, we check the ILR mode by examining the residual distribution based on skewness and kurtosis to determine whether the residuals follow a Gaussian distribution. In that case, the intra prediction comparisons, which require a high complexity, are skipped. Third, during the intra prediction selection from 35 Intra prediction modes, spatial and inter-layer correlations are combined with the local monotonicity of the Hadamard costs associated with the modes in a small neighborhood, to examine only a portion of the intra prediction modes. Finally, a hypothesis testing on the currently selected depth level is performed to examine whether the residuals present significant differences within their block to early terminate depth selection. The proposed multi-step multi-strategy scheme aims to minimize the number of depth selections while greatly reducing the mode decision complexity for a depth candidate in a hierarchical fashion. Our experimental results demonstrate that the proposed scheme can achieve a speedup gain of more than 75% in average on the test video sequences, while maintaining almost the same coding efficiency.
机译:作为高效视频编码(HEVC)的扩展,可伸缩高效视频编码(SHVC)引入了具有层间预测的多层,这大大增加了已经很复杂的HEVC编码器的复杂性。在质量SHVC的帧内预测中,编码树单元允许递归拆分为四个深度级别,该级别将考虑35种帧内预测模式和层间参考(ILR)模式,以确定每个深度级别的最佳可能模式。这实现了最高的编码效率,但是引起了相当高的计算复杂度。在本文中,我们提出了一种新颖的帧内预测方案,以有效地加速质量SHVC中的增强层帧内编码。所提出的框架的新特征包括:首先,空间相关性及其相关程度被组合以预测最可能的深度级别候选者。其次,对于给定的深度候选,基于ILR模式的概率,我们通过检查基于偏度和峰度的残差分布来确定残差是否遵循高斯分布,从而检查ILR模式。在那种情况下,跳过需要高复杂度的帧内预测比较。第三,在从35种帧内预测模式中选择帧内预测期间,将空间和层间相关性与与小邻域中的模式相关联的Hadamard成本的局部单调性组合在一起,以仅检查一部分帧内预测模式。最后,对当前选择的深度级别进行假设检验,以检查残差在其块内是否存在明显差异,以尽早终止深度选择。所提出的多步骤多策略方案旨在最小化深度选择的数量,同时以分层方式极大地降低深度候选者的模式决策复杂度。我们的实验结果表明,所提出的方案在测试视频序列上平均可以实现超过75%的加速增益,同时保持几乎相同的编码效率。

著录项

  • 来源
    《IEEE Transactions on Image Processing》 |2019年第4期|2063-2074|共12页
  • 作者单位

    Institute of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China;

    School of Electronic Engineering and the Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China;

    Department of Computer Science, University of Central Arkansas, Conway, AR, USA;

    Laboratoire des Signaux et Systèmes, CNRS CentraleSupelec-Université Paris-Sud, Gif-sur-Yvette, France;

    Department of Network Engineering, Chengdu University of Information Technology, Chengdu, China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Image coding; Correlation; Encoding; Video coding; Prediction algorithms; Complexity theory; Copper;

    机译:图像编码;相关性;编码;视频编码;预测算法;复杂度理论;铜;
  • 入库时间 2022-08-18 04:11:49

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