Although much work has been done toward developing lossless algorithms for compressing image data, most techniques reported have been for two-tone or gray-scale images. It is generally accepted that a color image can be easily encoded by using a gray-scale compression technique on each of the three (say, RGB) color planes. Such an approach, however, fails to take into account the substantial correlations that are present between color planes. Although several lossy compression schemes that exploit such correlations have been reported in the literature, we are not aware of any such techniques for lossless compression. Because of the difference in goals, the best ways of exploiting redundancies for lossy and lossless compression can be, and usually are, very different. We propose and investigate a few lossless compression schemes for RGB color images. Both prediction schemes and error modeling schemes are presented that exploit interframe correlations. Implementation results on a test set of images yield significant improvements.
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics