Reducing artifacts in JPEG decompression via a learned dictionary

Huibin Chang, Kwok Po NG, Tieyong ZENG

Research output: Contribution to journalArticlepeer-review

108 Citations (Scopus)

Abstract

The JPEG compression method is among the most successful compression schemes since it readily provides good compressed results at a rather high compression ratio. However, the decompressed result of the standard JPEG decompression scheme usually contains some visible artifacts, such as blocking artifacts and Gibbs artifacts (ringing), especially when the compression ratio is rather high. In this paper, a novel artifact reducing approach for the JPEG decompression is proposed via sparse and redundant representations over a learned dictionary. Indeed, an effective two-step algorithm is developed. The first step involves dictionary learning and the second step involves the total variation regularization for decompressed images. Numerical experiments are performed to demonstrate that the proposed method outperforms the total variation and weighted total variation decompression methods in the measure of peak of signal to noise ratio, and structural similarity.

Original languageEnglish
Article number6658879
Pages (from-to)718-728
Number of pages11
JournalIEEE Transactions on Signal Processing
Volume62
Issue number3
DOIs
Publication statusPublished - 1 Feb 2014

Scopus Subject Areas

  • Signal Processing
  • Electrical and Electronic Engineering

User-Defined Keywords

  • decompression
  • JPEG
  • learned dictionary
  • primal-dual algorithm
  • total variation

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