Abstract
Predictive coding has proven to be an effective method for lossless image compression. In predictive coding, untrans-mitted pixels are predicted based on the pixels already available at the decoder. Prediction errors are then compressed by entropy coding, and the original image can be reconstructed exactly at the decoder. More accurate prediction decreases the entropy of the prediction error, allowing for increased compression. Conventional image prediction methods rely on information from the immediate local neighborhood of each pixel. We introduce a novel predictor that leverages non-local structural similarities which have been shown to be effective in image denoising and deblurring applications. Experimental results show that the proposed method achieves state-of-the-art compression performance.
Original language | English (US) |
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Title of host publication | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 5636-5640 |
Number of pages | 5 |
ISBN (Print) | 9781479957514 |
DOIs | |
State | Published - Jan 28 2014 |
Keywords
- block matching
- causal prediction
- collaborative filtering
- Lossless compression
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition