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Modulus quantization for matching-pursuit video coding

机译:匹配追踪视频编码的模量量化

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Overcomplete signal decomposition using matching pursuits has been shown to be an efficient technique for coding motion-residual images in a hybrid video coder. Unlike orthogonal decomposition, matching pursuit uses an in-the-loop modulus quantizer which must be specified before coding begins. This complicates the quantizer design, since the optimal quantizer depends on the statistics of the matching-pursuit coefficients which in turn depend on the in loop quantizer actually used. In this paper, we address the modulus quantizer design issue, specifically developing frame-adaptive quantization schemes for the matching-pursuit video coder. Adaptive dead-zone subtraction is shown to reduce the information content of the modulus source, and a uniform threshhold quantizer is shown to be optimal for the resulting source. Practical two-pass and one-pass algorithms are developed to jointly determine the quantizer parameters and the number of coded basis functions in order to minimize coding distortion for a given rate. The compromise one-pass scheme performs nearly as well as the full two-pass algorithm, but with the same complexity as a fixed-quantizer design. The adaptive schemes are shown to outperform the fixed quantizer used in earlier works, especially at high bit rates, where the gain is as high as 1.7 dB.
机译:使用匹配追踪的过完全信号分解已被证明是一种用于在混合视频编码器中对运动残留图像进行编码的有效技术。与正交分解不同,匹配追踪使用必须在编码开始之前指定的环路模量量化器。这使量化器的设计复杂化,因为最佳量化器取决于匹配追踪系数的统计数据,而匹配追踪系数的统计又取决于实际使用的环路内量化器。在本文中,我们解决了模量量化器的设计问题,特别是针对匹配追踪视频编码器开发了帧自适应量化方案。自适应死区减法显示减少了模量源的信息内容,并且统一的阈值量化器对于结果源显示最佳。开发了实用的两遍和一遍算法,以共同确定量化参数和编码基函数的数量,以便在给定速率下将编码失真最小化。折衷的一遍方案的性能几乎与完整的二遍算法一样好,但其复杂度与固定量化器设计相同。自适应方案表现出优于早期工作中使用的固定量化器,特别是在高比特率时,增益高达1.7 dB。

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