Multimedia Department, IBM Research, Yorktown Heights, NY 10598, USA.
IEEE Trans Image Process. 2002;11(11):1228-37. doi: 10.1109/TIP.2002.804528.
This paper is concerned with developing a lossless image compression method which employs an optimal amount of segmentation information to exploit spatial redundancies inherent in image data. Multiscale segmentation is obtained using a previously proposed transform which provides a tree-structured segmentation of the image into regions characterized by grayscale homogeneity. In the proposed algorithm we prune the tree to control the size and number of regions thus obtaining a rate-optimal balance between the overhead inherent in coding the segmented data and the coding gain that we derive from it. Another novelty of the proposed approach is that we use an image model comprising separate descriptions of pixels lying near the edges of a region and those lying in the interior. Results show that the proposed algorithm can provide performance comparable to the best available methods and 15-20% better compression when compared with the JPEG lossless compression standard for a wide range of images.
本文致力于开发一种无损图像压缩方法，该方法利用最佳数量的分割信息来利用图像数据中固有的空间冗余。多尺度分割是通过先前提出的变换获得的，该变换提供了将图像分割成具有灰度均匀性的区域的树状结构。在提出的算法中，我们修剪树以控制区域的大小和数量，从而在对分段数据进行编码所固有的开销和我们从中获得的编码增益之间取得最佳的速率平衡。所提出方法的另一个新颖之处在于，我们使用包含位于区域边缘附近的像素和位于区域内部的像素的单独描述的图像模型。结果表明，所提出的算法可以提供与现有最佳方法相当的性能，并且与 JPEG 无损压缩标准相比，在广泛的图像中可以提供 15-20%更好的压缩。
