Multidirectional Gradient Adjusted Predictor

In this paper we investigate the prediction scheme of Context Based Adaptive Lossless Image Coding (CALIC), the standard for lossless/near lossless image compression for continuous-tone finger-print images. We show that it is not sufficient to consider the prediction technique in a single direction for a fingerprint image as a whole for Gradient Adjusted Predictor (GAP). As a result, we propose an additional GAP scheme to achieve better speed and better prediction accuracy as and hence provide potential for further improvements in Lossless Image Compression. Experimental results indicate that the proposed scheme outperforms the existing GAP prediction for all the finger-print images tested, while the complexity of the prediction algorithm is improved by more than four times with the help of parallel implementation.