Complexity-Distortion Optimized Motion Estimation Algorithm with Fine-Granular Scalable Complexity

Li Zhang, Wen Gao
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Abstract

Video encoding now is being implemented in various computing platforms with different computing capability, the requirement on the encoding complexity is also different according to different applications. As the most computation-intensive part of video encoding, the ME (motion estimation) should have a scalable complexity. This paper proposes a ME algorithm with fine-granular scalable complexity, a more important feature of the proposed algorithm is that it seeks for the complexity-distortion optimization. The given computation budget will be allocated to each MB (macroblock) in one frame. Each MB will consume its allocated computation by a hybrid search pattern. Experimental results show that the proposed algorithm can get a better computation-distortion performance than the existing ME algorithms
具有细粒度可扩展复杂度的复杂度-畸变优化运动估计算法
视频编码目前在各种计算平台上实现,计算能力不同,不同的应用对编码复杂度的要求也不同。运动估计作为视频编码中计算量最大的部分,其复杂度应具有可扩展性。本文提出了一种具有细粒度可扩展复杂度的ME算法,该算法的一个更重要的特点是寻求复杂度失真优化。给定的计算预算将在一帧中分配给每个MB(宏块)。每个MB将通过混合搜索模式消耗其分配的计算。实验结果表明,该算法比现有的ME算法具有更好的计算失真性能
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