Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Reducing Artifacts in JPEG Decompression Via a Learned Dictionary

Language

English

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. © 1991-2012 IEEE.

Keywords

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

Publication Date

2014

Source Publication Title

IEEE Transactions On Signal Processing

Volume

62

Issue

3

Start Page

718

End Page

728

Publisher

Institute of Electrical and Electronics Engineers

DOI

10.1109/TSP.2013.2290508

Link to Publisher's Edition

http://dx.doi.org/10.1109/TSP.2013.2290508

ISSN (print)

1053587X

ISSN (electronic)

19410476

This document is currently not available here.

Share

COinS