Efficient image/video deblocking via sparse representation

Yi Wen Chiou, Chia Hung Yeh*, Li Wei Kang, Chia Wen Lin, Shu Jhen Fan-Jiang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based image/video deblocking framework via properly formulating deblocking as an MCA (morphological component analysis)-based image decomposition problem via sparse representation. The proposed method first decomposes an image/video frame into the low-frequency and high-frequency parts by applying BM3D (block-matching and 3D filtering) algorithm. The high-frequency part is then decomposed into a 'blocking component' and a 'non-blocking component' by performing dictionary learning and sparse coding based on MCA. As a result, the blocking component can be removed from the image/video frame successfully while preserving most original image/video details. Experimental results demonstrate the efficacy of the proposed algorithm.

Original languageEnglish
Title of host publication2012 IEEE Visual Communications and Image Processing, VCIP 2012
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE Visual Communications and Image Processing, VCIP 2012 - San Diego, CA, United States
Duration: 2012 Nov 272012 Nov 30

Publication series

Name2012 IEEE Visual Communications and Image Processing, VCIP 2012

Conference

Conference2012 IEEE Visual Communications and Image Processing, VCIP 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period2012/11/272012/11/30

Keywords

  • Blocking artifact
  • deblocking
  • dictionary learning
  • morphological component analysis
  • sparse representation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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