Zhang Ning, Wu Xiaolin
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada.
IEEE Trans Image Process. 2006 Jun;15(6):1379-88. doi: 10.1109/tip.2005.871116.
Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.
彩色马赛克图像的无损压缩带来了一个独特且有趣的问题，即空间交错的R、G、B样本的光谱去相关。我们研究了可逆无损光谱-空间变换，该变换可以消除光谱和空间域中的统计冗余，并发现一种称为Mallat小波包变换的特定小波分解方案非常适合对彩色马赛克数据进行去相关的任务。我们还提出了一种低复杂度的基于自适应上下文的哥伦布-莱斯编码技术，以压缩Mallat小波包变换的系数。在所提出的方法对彩色马赛克图像的无损压缩性能在现有无损图像编解码器中显然是迄今为止最好的。
