L2restoration of l-decoded images via soft-decision estimation

Jiantao Zhou, Xiaolin Wu, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

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 l2distortion metric if the bit rate decreases further. In this paper, we propose a new soft decoding approach to reduce the l2distortion 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.
Original languageEnglish
Article number6212357
Pages (from-to)4797-4807
Number of pages11
JournalIEEE Transactions on Image Processing
Volume21
Issue number12
DOIs
Publication statusPublished - 26 Nov 2012

Keywords

  • Context modeling
  • image restoration Markov random field
  • MRF
  • near-lossless image coding

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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