基于双起点十字搜索模型的自适应搜索算法

Adaptive Search Algorithm Based on Double-initial-cross Search Pattern

提出一种基于双起点十字搜索模型的自适应搜索算法。该算法采用大小十字搜索模型、八边形搜索模型和斜交叉十字搜索模型,根据不同情况,使用相应的搜索模型,同时采用中值预测及提前跳出策略,减少不必要的搜索。序列测试结果表明,与UMHexagonS算法相比,新算法在保持相当的峰值信噪比和比特率的情况下,运动估计时间平均节省了15.88%和15.94%。

This paper proposes an adaptive search algorithm based on double-initial-cross. The algorithm employs big-small cross search pattern, octagon and quasi-cross search pattern for adapting to different block content. Median prediction and early skip strategy are used to reduce unnecessary search. Compared with UMHexagonS, simulation results of the sequences present that this algorithm can save motion estimation time of average 15.88% and 15.94% while obtaining similar PSNR and Bitrate.