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Edge-directed prediction for lossless compression of natural images

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2 Author(s)
Xin Li ; Sharp Labs. of America, Camas, WA, USA ; Orchard, M.T.

This paper sheds light on the least-square (LS)-based adaptive prediction schemes for lossless compression of natural images. Our analysis shows that the superiority of the LS-based adaptation is due to its edge-directed property, which enables the predictor to adapt reasonably well from smooth regions to edge areas. Recognizing that LS-based adaptation improves the prediction mainly around the edge areas, we propose a novel approach to reduce its computational complexity with negligible performance sacrifice. The lossless image coder built upon the new prediction scheme has achieved noticeably better performance than the state-of-the-art coder CALIC with moderately increased computational complexity

Published in:

Image Processing, IEEE Transactions on  (Volume:10 ,  Issue: 6 )