Nonlocal context modeling and adaptive prediction for lossless image coding

Abstract

Properly designed context models can increase the compression gain. In this paper, we propose a new lossless image coding scheme with two proposed algorithms: nonlocal context modeling and adaptive prediction (NCMAP). Since structural self-similarity often exists in natural images, we use the probability to measure the similarity between the powers of prediction errors for the pixels to be coded. Furthermore, the spatial distance and the intensity range are also considered for context generation. Moreover, a prediction scheme that adaptively combines the weighted edge-directed prediction (WEDP) and the nonlocal predictor (NLP) is also proposed. With the proposed context generating and prediction strategies, better compression performances can be achieved. Simulations show that the proposed scheme outperforms existing methods for lossless image compression.