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COMPLEXITY-DISTORTION OPTIMIZED MOTION ESTIMATION ALGORITHM WITH FINE-GRANULAR SCALABLE COMPLEXITY

机译:具有细粒度可扩展复杂性的复杂性失真优化运动估计算法

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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.
机译:现在正在在具有不同计算能力的各种计算平台中实现视频编码,根据不同的应用程序对编码复杂度的要求也不同。作为视频编码的最多计算密集型部分,我(运动估计)应该具有可扩展的复杂性。本文提出了一种具有细粒度可扩展复杂性的ME算法,所提出的算法的更重要的特征是它寻求复杂性失真优化。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,该算法可以获得比现有ME算法更好的计算失真性能。

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