Edge directed prediction for lossless compression of natural images

Natural images are populated with edges characterized by abrupt changes of local statistics. They put severe challenges on probability modeling of image sources. This paper proposes to employ recursive least square (RLS)-based predictive modeling to characterize local statistics for edges. It can be viewed as estimating the covariance matrix from a local causal neighborhood and selecting the MMSE optimal predictor for the local covariance estimate. We demonstrate how the RLS-based adaptation can produce predictor with support ideally aligned along an arbitrarily-oriented edge and therefore we call it "Edge Directed Prediction"(EDP). When applied to lossless image compression, the EDP substantially outperforms former context-based prediction schemes for natural images. Based on our high-level understanding of EDP, we dramatically reduce its complexity with little sacrifice on the performance, thus facilitating its application in practice.