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A hybrid block-based motion estimation algorithm using JAYA for video coding techniques

机译:基于混合块的运动估计算法使用Jaya进行视频编码技术

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In any video coding solution, motion estimation (ME) and motion compensation (MC) techniques are widely used and they play an inevitable role in reducing the temporal redundancies between successive frames. Block-based motion estimation (BME) is one of the widely utilized techniques in the recently developed video compression standards by JCT-VC including HEVC due to its efficacy and ease of implementation. In BME, the frames in a video sequence are partitioned into a number of non-overlapping blocks. Then, for each of the blocks, a best-matched block is obtained within a definite search region in the reference frame to minimize certain cost function or fitness value such as the sum of absolute difference (SAD), mean of absolute difference (MAD), or mean square error (MSE). However, the key challenge lies in the evaluation of these cost functions, since they are computationally expensive and involves the most time-taking operations in the BME process. Hence, BME-based approaches can be viewed as an optimization problem and meta-heuristic algorithms can be effectively exploited for the problem under consideration. In this paper, a hybrid BME technique using a recently developed optimization algorithm, namely, JAYA algorithm, is proposed. Besides this proposal, several other modules, namely, the fitness approximation technique, search history preservation, and early & adaptive termination criterion are utilized. The main objective behind the use of the aforementioned modules is to avoid the unnecessary evaluation of the fitness function. Exhaustive simulations are carried out to demonstrate the efficacy of the proposed method over that of the benchmark schemes with respect to different performance measures, namely, the peak signal-to-noise ratio (PSNR), PSNR degradation ratio (DpsNR). search efficiency, structural similarity index measure (SSIM), and computation time. Comparative analysis and quantitative evaluation clearly show that the present technique produces mor
机译:在任何视频编码解决方案中,广泛使用运动估计(ME)和运动补偿(MC)技术,并且它们在减少连续帧之间的时间冗余时发挥不可避免的作用。基于块的运动估计(BME)是由于其功效和易于实现而包括HEVC,包括HEVC最近开发的视频压缩标准中广泛利用的技术之一。在BME中,视频序列中的帧被划分为多个非重叠块。然后,对于每个块中,在参考帧中的明确搜索区域内获得最佳匹配的块,以最小化某些成本函数或适应性值,例如绝对差(SAD)的总和,绝对差异(MAD)的平均值,或均值方误差(MSE)。然而,关键挑战在于评估这些成本函数,因为它们是计算昂贵的并且涉及BME过程中最具时间的操作。因此,可以将基于BME的方法视为优化问题,并且可以有效地利用所考虑的问题的元启发式算法。本文提出了一种利用最近开发的优化算法的混合BME技术,即Jaya算法。除了这个提议之外,还使用了几个其他模块,即健身逼近技术,搜索历史保存和早期和自适应终止标准。使用上述模块的主要目标是避免不必要的对健身功能的评估。进行详尽的模拟,以证明所提出的方法在相对于不同性能测量的基准方案的功效,即峰值信噪比(PSNR),PSNR降解比(DPSNR)。搜索效率,结构相似性指数测量(SSIM)和计算时间。比较分析和定量评估清楚地表明本技术产生了MOR

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