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ADAPTIVE MCTF BASED ON CORRELATION NOISE MODEL FOR SNR SCALABLE VIDEO CODING

机译:基于SNR可伸缩视频编码的相关噪声模型的自适应MCTF

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This paper proposes a subband adaptive motion compensated temporal filtering (MCTF) technique for scalable video coding and introduces a revised synthesis gain model for the quantization in this adaptive MCTF scheme. In scalable video coding, hierarchical MCTF is extensively adopted to exploit the temporal correlation across video frames. In this hierarchical MCTF structure, the strength of temporal correlation varies with the level of temporal transform and varies with the various spatial frequency components in a frame. The reconstruction noises also have diverse strength at various subbands. According to the correlation and noise characteristics of various subbands, we can adjust the strength of motion compensated prediction step in MCTF to maximally take the advantage of temporal correlation but restrict the propagation of reconstruction noise. The quantization step of each subband is also adjusted according to synthesis gain determined by the MCTF structure. In this way an adaptive MCTF scheme is formed and the proposed technique improves the coding performance of scalable video coding.
机译:本文提出了一种用于可伸缩视频编码的子带自适应运动补偿时间滤波(MCTF)技术,并引入了这种自适应MCTF方案中的量化的修改合成增益模型。在可伸缩视频编码中,广泛采用分层MCTF来利用视频帧的时间相关性。在该层级MCTF结构中,时间相关强度随时间变换的水平而变化,并且随着帧中的各种空间频率分量而变化。在各种子带上的重建噪声也具有不同的强度。根据各种子带的相关性和噪声特性,我们可以调整MCTF中的运动补偿预测步骤的强度,以便最大地采取时间相关的优点,但限制了重建噪声的传播。还根据由MCTF结构确定的合成增益来调整每个子带的量化步骤。以这种方式形成自适应MCTF方案,并且所提出的技术改善了可伸缩视频编码的编码性能。

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