Quantization Context Two - row Double Buffer Error Modeling Gradient - adjusted Prediction Probabilities Estimation Conditional Histogram Coding

1 Summary We propose a context-based, adaptive, predictive coding system for lossless/nearly-lossless compression of continuous-tone images. The system provides better compression than other lossless image coders in the literature. This is accomplished with low time and space complexities. The high coding eeciency of the proposed image compression system is due to the use of a novel, nonlinear, context-based predictor; the low time and space complexities are made possible by an eecient technique for forming and quantizing prediction contexts. The proposed coding system gives an average lossless bit rate of 2.99 bits/pixel on the 18 8-bit test images selected by ISO for proposal evaluation, versus an average bit rate of 3.98 bits/pixel for lossless JPEG on the same set of test images (we cannot compare the two methods on images of higher than 8 bit intensity resolutions, because there is no available implementation of lossless JPEG to handle these images). Even more encouragingly, our system obtains 1 % lower bit rate than the recent UCM (Universal Context Modeling) method 6]. The latter is a highly sophisticated and complex context-based image coding technique which was considered to be indicative of lower bound on lossless bit rates achievable by other more practical methods. Furthermore, in its nearly lossless version, the proposed image coder yields signiicantly higher compression than the The contact person.