Laplace Distribution Based Lagrangian Rate Distortion Optimization for Hybrid Video Coding

摘要

In today's hybrid video coding, Rate-Distortion Optimization (RDO) plays a critical role. It aims at minimizing the distortion under a constraint on the rate. Currently, the most popular RDO algorithm for one-pass coding is the one recommended in the H.264/AVC reference software. It, or HR-$lambda $ for convenience, is actually a kind of universal method which performs the optimization only according to the quantization process while ignoring the properties of input sequences. Intuitively, it is not efficient all the time and an adaptive scheme should be better. Therefore, a new algorithm Lap- $lambda $ is presented in this paper. Based on the Laplace distribution of transformed residuals, the proposed Lap-$lambda $ is able to adaptively optimize the input sequences so that the overall coding efficiency is improved. Cases which cannot be well captured by the proposed models are considered via escape methods. Comprehensive simulations verify that compared with HR-$lambda $ , Lap-$lambda $ shows a much better or similar performance in all scenarios. Particularly, significant gains of 1.79 dB and 1.60 dB in PSNR are obtained for slow sequences and B-frames, respectively.