基于统计学习的H.264/AVC快速帧间模式选择方法

Statistical Learning Based Fast Mode Selection Scheme For H.264/AVC Inter Prediction

H.264采用了可变块大小的运动补偿和基于率失真优化的模式选择来提高压缩比和视频质量,这些技术使H.264比其他现有的编码标准都要好,然而,却计算强度大而且非常耗时。本文提出了一种快速的帧间模式选择方法。首先,先编码的几帧作为统计学习对象,得到相应的阈值;然后,后面的帧就根据这些阈值来选择备选模式,只对已选的模式进行运动估计。本文提出的方法适用于所有现有的运动搜索算法,而且阈值是针对每个序列线上计算的。结果显示,本文的方法可使编码时间平均降低57.2%,而对视频质量的降级可忽略。

H. 264 adopts variable block size motion estimation and Rate - Distortion - Optimization based mode decision to improve vid- eo quality and compression ratio. These techniques have made H. 264 better than other existing video coding standards. However, they are computationally intensive and time - consuming. In this paper, a fast mode selection scheme is proposed for H. 264 inter predic- tion. Firstly, the first few frames are encoded and thresholds are acquired through a statistical learning process. Then, for the rest of frames, motion estimation and mode decision are only performed for the candidate modes which are selected with the proposed fast mode selection scheme. The proposed approach is applicable to all existing motion search algorithms. Besides, thresholds are on - line com- puted separately for each sequence. Results show that the total encoding time is saved by 57.2% on average with negligible video qual- ity degradation.