A high quality hardware friendly motion estimation algorithm focusing in HD videos

Pargles Dall'Oglio, Cassio Cristani, M. Porto, L. Agostini
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引用次数: 0

Abstract

Fast algorithms for motion estimation are often trapped in local minima, especially when working on high definition videos (HD). This paper presents a new algorithm for motion estimation focused on high definition videos named Multiple Iterative Random Search (MIRS). This algorithm uses randomness and multiple iterative steps as strategy to avoid local minima falls, achieving better quality results than traditional fast algorithms. MIRS is a hardware friendly algorithm since it has five iterative steps which did not have data dependencies, then these five iterative steps can be implemented in parallel, reaching a processing rate similar to other fast algorithms. That characteristic becomes MIRS a very competitive option for hardware implementation, since it is possible to reach very high processing rates with very good quality results. The comparative results show that MIRS algorithm presented the best PSNR among all evaluated fast algorithms. MIRS is also able to reduce in 70 times the number of evaluated blocks when compared with Full Search algorithm with a PSNR drop of only 0.71dB.
一个高质量的硬件友好的运动估计算法,专注于高清视频
快速运动估计算法经常陷入局部最小值,特别是在处理高清视频(HD)时。本文提出了一种针对高清视频的运动估计新算法——多重迭代随机搜索算法。该算法利用随机性和多迭代步骤作为策略,避免了局部最小值下降,获得了比传统快速算法更好的结果质量。MIRS是一种硬件友好的算法,因为它有五个迭代步骤,没有数据依赖关系,所以这五个迭代步骤可以并行实现,达到与其他快速算法相似的处理速度。这一特性使MIRS成为硬件实现的一个非常有竞争力的选择,因为可以达到非常高的处理速率和非常好的质量结果。对比结果表明,在所有被评价的快速算法中,MIRS算法的PSNR最好。与Full Search算法相比,MIRS还能够减少70倍的评估块数量,而PSNR仅下降0.71dB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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