A New Adaptive Blending Predictor for Lossless Image Compression

Summary form only given. In the paper we propose a new adaptive predictor which is based on the blending of multiple static predictors on the dynamically classified causal set of neighboring pixels. The predictor models the image properties around the current, unknown pixel and adjusts itself to the local image region. The main contribution of this work is the enhancement of the well known approach of predictor blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique. This allows modeling of more complex image structures such as nontrivially oriented edges and the periodicity and coarseness of textures.