Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21117
Title: L2 restoration of l∞-decoded images via soft-decision estimation
Authors: Zhou, J
Wu, X
Zhang, L 
Keywords: Context modeling
image restoration Markov random field
MRF
near-lossless image coding
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2012, v. 21, no. 12, 6212357, p. 4797-4807 How to cite?
Journal: IEEE transactions on image processing 
Abstract: The l∞-constrained image coding is a technique to achieve substantially lower bit rate than strictly (mathematically) lossless image coding, while still imposing a tight error bound at each pixel. However, this technique becomes inferior in the l2 distortion metric if the bit rate decreases further. In this paper, we propose a new soft decoding approach to reduce the l2 distortion of l∞-decoded images and retain the advantages of both minmax and least-square approximations. The soft decoding is performed in a framework of image restoration that exploits the tight error bounds afforded by the l∞-constrained coding and employs a context modeler of quantization errors. Experimental results demonstrate that the l∞-constrained hard decoded images can be restored to gain more than 2 dB in peak signal-to-noise ratio PSNR, while still retaining tight error bounds on every single pixel. The new soft decoding technique can even outperform JPEG 2000 (a state-of-the-art encoder-optimized image codec) for bit rates higher than 1 bpp, a critical rate region for applications of near-lossless image compression. All the coding gains are made without increasing the encoder complexity as the heavy computations to gain coding efficiency are delegated to the decoder.
URI: http://hdl.handle.net/10397/21117
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2012.2202672
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