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Neyman-Pearson-Based Early Mode Decision for HEVC Encoding

机译：基于Neyman-Pearson的HEVC编码的早期模式决策

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摘要

The high efficiency video coding (HEVC) standard has highly improved the coding efficiency by adopting hierarchical structures of coding unit (CU), prediction unit (PU), and transform unit (TU). However, enormous computational complexity is introduced due to the recursive rate-distortion optimization (RDO) process on all CUs, PUs and TUs. In this paper, we propose a fast and efficient mode decision algorithm based on the Neyman-Pearson rule, which consists of early SKIP mode decision and fast CU size decision. First, the early mode decision is modeled as a binary classification problem of SKIPon-SKIP or split/unsplit. The Neyman-Pearson-based rule is employed to balance the rate-distortion (RD) performance loss and the complexity reduction by minimizing the missed detection with a constrained incorrect decision rate. A nonparametric density estimation scheme is also developed to calculate the likelihood function of the statistical parameters. Furthermore, an online training scheme is employed to periodically update the probability density distributions for different quantization parameters (QPs) and CU depth levels. The experimental results show that the proposed overall algorithm can save 65% and 58% computational complexity on average with a 1.29% and 1.08% Bjontegaard Delta bitrate (BDBR) increase for various test sequences under random access and low delay P conditions, respectively. The proposed overall scheme also has the advantage that it can make the trade-off between the RD performance and time saving by setting different values for the incorrect decision rate.
机译：高效视频编码（HEVC）标准通过采用编码单元（CU），预测单元（PU）和变换单元（TU）的分层结构，极大地提高了编码效率。但是，由于对所有CU，PU和TU进行了递归率失真优化（RDO）处理，因此引入了巨大的计算复杂性。本文提出了一种基于Neyman-Pearson规则的快速高效的模式决策算法，该算法包括早期的SKIP模式决策和快速的CU大小决策。首先，将早期模式决策建模为SKIP /非SKIP或拆分/未拆分的二进制分类问题。基于Neyman-Pearson的规则用于通过限制错误的决策率来最小化漏检，从而平衡速率失真（RD）性能损失和复杂性降低。还开发了非参数密度估计方案来计算统计参数的似然函数。此外，采用在线培训方案来定期更新不同量化参数（QP）和CU深度级别的概率密度分布。实验结果表明，所提出的总体算法在随机访问和低延迟P条件下，各种测试序列的平均Bjontegaard Delta比特率（BDBR）增加了1.29％和1.08％，平均节省了65％和58％的计算复杂度。所提出的总体方案还具有以下优点：可以通过为错误的决策率设置不同的值来在RD性能与节省时间之间进行权衡。

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