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Motion-vector optimization of control grid interpolation and overlapped block motion compensation using iterated dynamic programming

机译:使用迭代动态规划的控制网格插值和重叠块运动补偿的运动矢量优化

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摘要

The application of advanced motion compensation techniques-control grid interpolation (CGI) and overlapped block motion compensation (OBMC)-to video coding systems provides significant performance advantages, terms of compression ratio and visual quality, over traditional block-matching motion compensation. However, the two-dimensional (2-D) interdependence among motion vectors introduced by these compensation frameworks makes the problem of finding rate-distortion optimal motion vectors, computationally prohibitive. Thus, iterative optimization techniques are often used to achieve good compensation performance. While most reported optimization algorithms adopt an approach that uses a block-matching algorithm to obtain an initial estimate and then successively optimize each motion vector, the over-relaxed motion-vector dependency relations often result in considerable performance degradation. In view of this problem, we present a new optimization scheme for dependent motion-vector optimization problems, one based on dynamic programming. Our approach efficiently decomposes 2-D dependency problems into a series of one-dimensional (1-D) dependency problems. We show that a reliable initial estimate of motion vectors can be obtained efficiently by only considering the dependency in the rate term. We also show that at the iterative optimization stage an effective logarithmic search strategy can be used with dynamic programming to reduce the necessary complexity involved in distortion computation. Compared to conventional iterative approaches, our experimental results demonstrate that our algorithm provides superior rate and distortion performance while maintaining reasonable complexity.
机译:与传统的块匹配运动补偿相比,将先进的运动补偿技术(控制网格插值(CGI)和重叠块运动补偿(OBMC))应用于视频编码系统具有明显的性能优势,包括压缩率和视觉质量。然而,由这些补偿框架引入的运动向量之间的二维(2-D)相互依赖性使得寻找速率失真最优运动向量的问题变得计算上难以实现。因此,经常使用迭代优化技术来实现良好的补偿性能。尽管大多数报告的优化算法都采用了一种方法,该方法使用块匹配算法来获取初始估计值,然后依次优化每个运动矢量,但是过松弛的运动矢量依赖关系通常会导致性能显着下降。针对这一问题,我们提出了一种基于动态规划的针对运动矢量优化问题的优化方案。我们的方法有效地将2D依赖性问题分解为一系列一维(1-D)依赖性问题。我们表明,仅考虑速率项的相关性,就可以有效地获得运动矢量的可靠初始估计。我们还表明,在迭代优化阶段,可以将有效的对数搜索策略与动态规划一起使用,以减少失真计算中涉及的必要复杂性。与传统的迭代方法相比,我们的实验结果表明,我们的算法在保持合理复杂度的同时提供了出色的速率和失真性能。

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