Digital video technology has been increasingly needed in various fields, such as telecommunications, entertainment, medicine. Therefore, video compression is required. Motion estimation methods help in improving video compression efficiency by effectively removing the temporal redundancy between successive frames. Several block-based motion estimation (BME) algorithms are being suggested to reduce the coding process’s computational complexity. This paper proposes a new rapid hybrid (BME) algorithm established on the primary search point prediction and advance ending search point strategies. It combines rough adaptive search and effective local search. The coarse search introduces a new motion vector (MV) prediction technique that utilizes the macro-blocks (MBs) Spatio-temporal correlations to optimize the traditional adaptive-rood-pattern search algorithm (ARPS) and speeding up the whole process without affecting the accuracy. In the accurate local search, the cross-formed search pattern using a one-step search (OSS) block matching algorithm is employed, to estimate the actual (MV) with less computation time and further speed up the search efficiency. Exhaustive experiments are performed to demonstrate the present algorithm’s performance over the benchmark schemes concerning specific assessment criteria for results, including the peak signal-to-noise ratio (PSNR), computational complexity and computational gain. The results show that the proposed algorithm is efficient and reliable; it can always give better performance over diamond search (DS) and (ARPS). The conducted test shows an increased performance of search speed while preserving the visual quality of the motion-compensated images, and it achieves 59.76–88.03 speed improvement over (DS) and 20.98–72.06 over (ARPS) for different video sequences. Besides, the suggested method (ARP-OSS) provides the best result compared to (DS) and (ARPS) in terms of time complexity for analyzing all the video samples.

中文翻译：

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用于帧间编码的改进的基于混合块的运动估计

数字视频技术在电信、娱乐、医疗等各个领域越来越需要。因此，需要进行视频压缩。运动估计方法通过有效去除连续帧之间的时间冗余来帮助提高视频压缩效率。正在建议几种基于块的运动估计 (BME) 算法来降低编码过程的计算复杂性。本文提出了一种基于主搜索点预测和提前结束搜索点策略的快速混合（BME）算法。它结合了粗略的自适应搜索和有效的局部搜索。粗搜索引入了一种新的运动矢量（MV）预测技术，该技术利用宏块（MB）时空相关性来优化传统的自适应模式搜索算法（ARPS），并在不影响准确性的情况下加快整个过程. 在精确的局部搜索中，采用使用一步搜索（OSS）块匹配算法的交叉搜索模式，以较少的计算时间估计实际（MV），进一步提高搜索效率。进行了详尽的实验以证明本算法在涉及结果的特定评估标准的基准方案上的性能，包括峰值信噪比 (PSNR)、计算复杂度和计算增益。结果表明，所提算法高效可靠；它总是可以提供比钻石搜索 (DS) 和 (ARPS) 更好的性能。进行的测试表明，在保持运动补偿图像的视觉质量的同时，搜索速度的性能有所提高，并且对于不同的视频序列，速度比 (DS) 提高了 59.76-88.03，比 (ARPS) 提高了 20.98-72.06。此外，在分析所有视频样本的时间复杂度方面，建议的方法 (ARP-OSS) 与 (DS) 和 (ARPS) 相比提供了最好的结果。