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MapReduce analytics-based fast search algorithms

机译:MapReduce基于分析的快速搜索算法

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摘要

Using the motion estimation process is still one of the best technologies for video coding standards such as H.26X or MPEG-X. These standards have been supplied to video compression, but this area is still open algorithms. It can provide excellent coding efficiency while maintaining a low distortion. A new motion estimation of a full search algorithm with big data analytics in this research was proposed, which uses MapReduce analysis metrics. The developed method can not only be used to reduce the stored number of motion vectors compared with the full search algorithm but also to perform superior peak signal-to-noise ratio than other recent developed scheme, and achieve the same peak signal-to-noise ratio as the full search algorithm. Simulation exemplifies that our proposed scheme obtains better improvement than the full search algorithm and other well-known methods. The comparison of our proposed algorithms with other methods is evaluated both subjectively and qualitatively and the simulation demonstrated that our proposed method could obtain the desired reconstructed performance subjectively and objectively.
机译:使用运动估计过程仍然是H.26X或MPEG-X等视频编码标准的最佳技术之一。这些标准已经提供给视频压缩,但这一领域仍然是开放的算法。它可以在保持低失真的同时提供出色的编码效率。本文提出了一种基于大数据分析的全搜索运动估计算法,该算法使用MapReduce分析度量。与全搜索算法相比,该方法不仅可以减少运动矢量的存储数量,而且与最近开发的其他方案相比,具有更好的峰值信噪比,并实现与全搜索算法相同的峰值信噪比。仿真结果表明,与全搜索算法和其他著名的算法相比,我们提出的方案得到了更好的改进。从主观和定性两方面对我们提出的算法与其他方法进行了比较,仿真结果表明,我们提出的方法能够主观和客观地获得所需的重构性能。

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