Edge-directed prediction for lossless compression of natural images

This paper sheds light on the recent least-square (LS)-based adaptive predi ction schemes for lossless compression of natural images. Our analysis show s that the superiority of the LS-based adaptation is due to its edge-direct ed property, which enables the predictor to adapt reasonably well from smoo th regions to edge areas. Recognizing that LS-based adaptation improves the prediction mainly around the edge areas, we propose a novel approach to re duce its computational complexity with negligible performance sacrifice. Th e lossless image coder built upon the new prediction scheme has achieved no ticeably better performance than the state of-the-art coder CALIC with mode rately increased computational complexity.