帧内/帧间子带系统的编码增益

G. Galvagno, G. Mian, R. Rinaldo
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引用次数: 1

摘要

只提供摘要形式。典型的图像序列编码器使用运动补偿技术来对运动补偿的差分图像进行编码(帧间编码)。此外,差分循环通过帧内图像编码不时初始化。因此,重要的是要有一个程序,允许评估一个特定的编码方案的性能:编码增益和率失真图在这项工作中用于此目的。我们提出了一个明确的程序来计算二维可分离子带系统的编码增益,在均匀和金字塔子带分解的情况下,以及在帧间编码的情况下。该技术在信号域中操作,需要了解输入过程的自相关函数。在可分离子带系统和图像频谱的情况下,可以通过结合相对于适当定义的一维滤波方案的结果来计算编码增益,从而使该技术在计算复杂性方面非常有吸引力。我们考虑了均匀子带分解和金字塔分解的情况。所开发的程序应用于计算运动补偿信号的子带编码增益,在图像建模为可分离的马尔可夫过程的情况下:不同的滤波器组相互比较并变换编码。为了表明运动补偿的有效性,我们还计算了帧内图像的编码增益。我们表明,图像模型的结果与实际数据的结果非常吻合。
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Coding gain of intra/inter-frame subband systems
Summary form only given. Typical image sequence coders use motion compensation techniques in connection to coding of the motion compensated difference images (interframe coding). Moreover, the difference loop is initialized from time to time by intraframe coding of images. It is therefore important to have a procedure that allows to evaluate the performance of a particular coding scheme: coding gain and rate-distortion figures are used in this work to this purpose. We present an explicit procedure to compute the coding gain for two-dimensional separable subband systems, both in the case of a uniform and a pyramid subband decomposition, and for the case of interframe coding. The technique operates in the signal domain and requires the knowledge of the autocorrelation function of the input process. In the case of a separable subband system and image spectrum, the coding gain can be computed by combining the results relative to appropriately defined one-dimensional filtering schemes, thus making the technique very attractive in terms of computational complexity. We consider both the case of a uniform subband decomposition and of a pyramid decomposition. The developed procedure is applied to compute the subband coding gain for motion compensated signals in the case of images modeled as separable Markov processes: different filter banks are compared to each other and to transform coding. In order to have indications on the effectiveness of motion compensation, we also compute the coding gain for intraframe images. We show that the results for the image models are in very good agreement with those obtained with real-world data.
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