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A fast coding unit division and mode selection method for HEVC intra prediction

机译:HEVC帧内预测的快速编码单位划分与模式选择方法

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The high efficiency video coding standard (HEVC) has come into view in recent years. In which some specific process and details were introduced to achieve higher coding efficiency, but lead to a very high computational complexity. In this paper, we focus on the need of lower complexity in computing recourse limited environment, and present a fast CU division algorithm and mode selection method for HEVC intra prediction. Experimental results show that the rate distortion cost (RD-Cost) values of no-split CU are clearly lower than that of splitting. So we formulate threshold equations of different depth level in the coding tree, by statistical analyzing of the RD-Cost values under different quantization parameter (QP) probabilities distribution. Division of code unit could be terminated earlier using threshold, which shall reduce the computing complexity. The experimental results show that, compared with HEVC testing model (HM), the improved algorithm can save an average 27.6% of encoding time with negligible loss of coding efficiency (only 0.49% bitrate increasing, and 0.013dB Peak Signal-to-Noise Ratio (Y-PSNR) loss). Meanwhile, by fully exploiting the correlation between the first rank prediction mode of the candidate mode set and the optimal prediction mode, some prediction modes can be skipped. It also leads to the decrease of computing complexity and the experimental results show that the proposed algorithm saves 42.1% of encoding time for the premise of ensuring the video quality.
机译:近年来,高效视频编码标准(HEVC)出现了。其中引入了一些特定的过程和细节来实现更高的编码效率,但是却导致了很高的计算复杂度。在本文中,我们着重于在计算资源受限的环境中降低复杂度的需求,并提出了一种用于HEVC帧内预测的快速CU划分算法和模式选择方法。实验结果表明,未拆分CU的速率失真成本(RD-Cost)值明显低于拆分。因此,我们通过对不同量化参数(QP)概率分布下RD-Cost值的统计分析,在编码树中制定了不同深度级别的阈值方程。使用阈值可以更早地终止代码单元的划分,这将降低计算复杂度。实验结果表明,与HEVC测试模型(HM)相比,改进后的算法平均可节省编码时间27.6%,而编码效率的损失可忽略不计(仅增加0.49%的比特率和0.013dB的峰值信噪比) (Y-PSNR)损失)。同时,通过充分利用候选模式集的第一等级预测模式与最佳预测模式之间的相关性,可以跳过一些预测模式。实验结果表明,该算法在保证视频质量的前提下,节省了编码时间42.1%。

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