Lossless image compression by using gradient adjusted prediction and Burrows-Wheeler transformation

In general text compression techniques cannot be used directly in image compression because the model of text and image are different. Previously, a new class of text compression, namely, the block-sorting algorithm which involves Burrows and Wheeler (1994) transformation (BWT) gave excellent results in text compression. However, if we apply it directly in image compression, the result is poor. Surprisingly, good results can be obtained if we employ a prediction model such as the one defined in the JPEG standard before the BWT algorithm. Thus, the predictive model plays a critical role in the compression process. To further improve the compression efficiency, we use the gradient adjusted prediction (GAP). Experimental results show that the proposed method is better than lossless JPEG and some LZ-based compression methods.