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A New Rate-Complexity-Distortion Model for Fast Motion Estimation Algorithm in HEVC

机译:HEVC中快速运动估计算法的速率复杂度失真模型

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In the high efficiency video coding (HEVC) standard, motion estimation (ME) adopts a quadtree coding structure and a larger search range to improve the coding performance. These advanced coding tools, however, dramatically increase the computational complexity. To accelerate ME, fast methods have been proposed that reduce ME complexity at the expense of rate-distortion (R-D) performance. However, none of these methods can claim their tradeoff to be optimal. In this paper, we propose an optimal fast motion estimation (FME) algorithm based on an analytical model of rate-complexity-distortion (R-C-D). We extend the traditional R-D model by introducing the ME complexity into it, which enables us to explicitly express the R-D performance under different complexity budgets. Based on the R-C-D model, the proposed FME finds the R-C-D optimized search ranges for some representative prediction units (PUs), which are then extended or refined dynamically according to motion characteristics for neighboring PUs. The proposed FME enables an R-D performance close to that of full search. When compared with the default FME method in the reference software of HEVC, the proposed fast algorithm can reduce the complexity by over 80% while improving the R-D performance. Furthermore, our proposed FME algorithm is hardware friendly, as regular data flow enables high data reuse efficiency.
机译:在高效视频编码(HEVC)标准中,运动估计(ME)采用四叉树编码结构和较大的搜索范围以提高编码性能。但是,这些高级编码工具极大地增加了计算复杂性。为了加速ME,已经提出了以降低速率失真(R-D)性能为代价降低ME复杂性的快速方法。但是,这些方法都不能声称其折衷是最佳的。在本文中,我们提出了一种基于速率复杂度失真(R-C-D)分析模型的最优快速运动估计(FME)算法。我们通过引入ME复杂性来扩展传统的R-D模型,这使我们能够明确表示不同复杂性预算下的R-D性能。提出的FME基于R-C-D模型,找到了一些代表性预测单元(PU)的R-C-D优化搜索范围,然后根据相邻PU的运动特征对其进行动态扩展或细化。提议的FME使R-D性能接近于完全搜索。与HEVC的参考软件中的默认FME方法相比,该快速算法可以在提高R-D性能的同时将复杂度降低80%以上。此外,我们提出的FME算法对硬件友好,因为常规数据流可实现较高的数据重用效率。

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