Illinois Univ., Urbana, IL.
IEEE Trans Image Process. 1996;5(7):1197-204. doi: 10.1109/83.502409.
We introduce a novel, adaptive image representation using spatially varying wavelet packets (WPs), Our adaptive representation uses the fast double-tree algorithm introduced previously (Herley et al., 1993) to optimize an operational rate-distortion (R-D) cost function, as is appropriate for the lossy image compression framework. This involves jointly determining which filter bank tree (WP frequency decomposition) to use, and when to change the filter bank tree (spatial segmentation). For optimality, the spatial and frequency segmentations must be done jointly, not sequentially. Due to computational complexity constraints, we consider quadtree spatial segmentations and binary WP frequency decompositions (corresponding to two-channel filter banks) for application to image coding. We present results verifying the usefulness and versatility of this adaptive representation for image coding using both a first-order entropy rate-measure-based coder as well as a powerful space-frequency quantization-based (SPQ-based) wavelet coder introduced by Xiong et al. (1993).
我们提出了一种新颖的、自适应的图像表示方法，使用空间变化的小波包 (WP)。我们的自适应表示方法使用了先前引入的快速双树算法 (Herley 等人，1993) 来优化操作率失真 (R-D) 代价函数，这适合于有损图像压缩框架。这涉及联合确定要使用的滤波器组树 (WP 频率分解)，以及何时更改滤波器组树 (空间分割)。为了实现最优性，空间和频率分割必须联合进行，而不是顺序进行。由于计算复杂度的限制，我们考虑四叉树空间分割和二进制 WP 频率分解 (对应于双通道滤波器组)，用于图像编码。我们提出的结果验证了这种自适应表示方法对于使用基于一阶熵率测度的编码器以及由 Xiong 等人引入的强大的基于空间频率量化的 (SPQ 基) 小波编码器进行图像编码的有用性和多功能性。
