This article describes a framework to speed up the HEVC encoding decisions for on-demand transrating of bitstreams. The methods proposed collect information from a high-quality reference bitstream which after processing is used to limit the number of modes evaluated in subsequent re-encodings at different bitrates. In this way, the time required to process re-encode-time computing-intensive decisions, such as partitioning and motion estimation is significantly reduced. The methods proposed are a combination of heuristics with a statistical basis and fast decision techniques trained using automatic learning methodologies. Experimental results using the HEVC reference encoder show that jointly the methods proposed reduce the transcoding computational complexity by up to 78.8%, with Bjontegaard bitrate deltas penalties smaller than 1.06%. A comparison with related works showed that the proposed method is able to outperform state-of-the-art solutions in terms of combined rate-distortion–complexity performance indicators.

本文介绍了一种框架，该框架可加快HEVC编码决策的速度，以按需进行比特流转换。所提出的方法从高质量参考比特流收集信息，该高质量参考比特流在处理之后被用于限制以不同比特率在随后的重新编码中评估的模式的数量。以此方式，大大减少了处理重新编码时间的计算密集型决策（例如分区和运动估计）所需的时间。提出的方法是具有统计基础的启发式方法和使用自动学习方法训练的快速决策技术的组合。使用HEVC参考编码器进行的实验结果表明，所提出的方法共同将代码转换的计算复杂度降低了78.8％，而Bjontegaard比特率差值的惩罚小于1.06％。