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Fast mode decision for the H.264∕AVC video coding standard based on frequency domain motion estimation

机译:基于频域运动估计的H.264 / aVC视频编码标准的快速模式决策

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摘要

The intra-mode and inter-mode predictions have been made available in H.264/AVC for effectively improving coding efficiency. However, exhaustively checking for all the prediction modes for identifying the best one (commonly referred to as exhaustive mode decision) greatly increases computational complexity. In this paper, a fast mode decision algorithm, called the motion activity-based mode decision (MAMD), is proposed to speed up the encoding process by reducing the number of modes required to be checked in a hierarchical manner, and is as follows. For each macroblock, the proposed MAMD algorithm always starts with checking the rate-distortion (RD) cost computed at the SKIP mode for a possible early termination, once the RD cost value is below a predetermined ldquolowrdquo threshold. On the other hand, if the RD cost exceeds another ldquohighrdquo threshold, then this indicates that only the intra modes are worthwhile to be checked. If the computed RD cost falls between the above-mentioned two thresholds, the remaining seven modes, which are classified into three motion activity classes in our work, will be examined, and only one of the three classes will be chosen for further mode checking. The above-mentioned motion activity can be quantitatively measured, which is equal to the maximum city-block length of the motion vector taken from a set of adjacent macroblocks (i.e., region of support, ROS). This measurement is then used to determine the most possible motion-activity class for the current macroblock. Experimental results have shown that, on average, the proposed MAMD algorithm reduces the computational complexity by 62.96%, while incurring only 0.059 dB loss in PSNR (peak signal-to-noise ratio) and 0.19% increment on the total bit rate compared to that of exhaustive mode decision, which is a default approach set in the JM reference software.
机译:H.264 / AVC中提供了模式内和模式间预测,以有效提高编码效率。但是,穷举检查所有用于识别最佳预测模式的预测模式(通常称为穷举模式决策)会大大增加计算复杂性。在本文中,提出了一种快速模式决策算法,称为基于运动活动的模式决策(MAMD),它通过减少分层检查所需的模式数量来加快编码过程,具体如下。对于每个宏块,一旦RD成本值低于预定的“低阈值”,建议的MAMD算法始终以检查在SKIP模式下计算的速率失真(RD)成本为可能的提前终止开始。另一方面,如果RD成本超过另一个“高阈值”,则表明只有帧内模式值得检查。如果计算出的RD成本落在上述两个阈值之间,则将检查在我们的工作中分为三个运动活动类别的其余七个模式,并且将仅选择三个类别中的一个进行进一步的模式检查。可以定量地测量上述运动活动,其等于从一组相邻宏块(即,支撑区域,ROS)获取的运动矢量的最大城市块长度。然后,该测量用于确定当前宏块的最可能的运动活动类别。实验结果表明,平均而言,所提出的MAMD算法将计算复杂度降低了62.96%,而与之相比,PSNR(峰值信噪比)仅损失0.059 dB,总比特率增加了0.19%。穷举模式决策,这是JM参考软件中设置的默认方法。

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