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首页> 外文期刊>Journal of visual communication & image representation >A rate-distortion analysis on motion prediction efficiency and mode decision for scalable wavelet video coding
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A rate-distortion analysis on motion prediction efficiency and mode decision for scalable wavelet video coding

机译:可伸缩小波视频编码运动预测效率和模式决策的速率失真分析

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A rate-distortion model for describing the motion prediction efficiency in interframe wavelet video coding is proposed in this paper. Different from the non-scalable video coding, the scalable wavelet video coding needs to operate under multiple bitrate conditions and it has an open-loop structure. The conventional Lagrangian multiplier, which is widely used to solve the rate-distortion optimization problems in video coding, does not fit well into the scalable wavelet structure. In order to find the rate-distortion trade-off due to different bits allocated to motion and textual information, we suggest a motion information gain (MIG) metric to measure the motion prediction efficiency. Based on this metric, a new cost function for mode decision is proposed. Compared with the conventional Lagrangian method, our experiments show that the proposed method is less extraction-bitrate dependent and generally improves both the PSNR performance and the visual quality for the scalability cases.
机译:提出了一种描述帧间小波视频编码中运动预测效率的速率失真模型。与非可伸缩视频编码不同,可伸缩小波视频编码需要在多种比特率条件下运行,并且具有开环结构。传统的拉格朗日乘法器已被广泛用于解决视频编码中的速率失真优化问题,但无法很好地适应可伸缩小波结构。为了找到由于分配给运动和文本信息的不同位而导致的速率失真折衷,我们建议使用运动信息增益(MIG)度量来测量运动预测效率。基于该度量,提出了一种新的模式决策成本函数。与传统的拉格朗日方法相比,我们的实验表明,该方法对提取比特率的依赖性较小,并且在可扩展性情况下总体上提高了PSNR性能和视觉质量。

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