Blocking effect reduction of compressed images using classification-based constrained optimization

Tae Keun Kim, Joon Ki Paik, Chee Sun Won, Yoonsik Choe, Jechang Jeong, Jae Yeal Nam

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

In this paper we propose an adaptive image restoration algorithm using block-based edge-classification for reducing block artifacts in compressed images. In order to efficiently reduce block artifacts, edge direction of each block is classified by using model-fitting criterion, and the constrained least-squares (CLS) filter with corresponding direction is used for restoring the block. The proposed restoration filter is derived based on the observation that the quantization operation in a series of coding processes is a nonlinear and many-to-one mapping operator. Then we propose an approximated version of a constrained optimization technique as a restoration process for removing the nonlinear and space-varying degradation operator. For real-time implementation, the proposed restoration filter can be realized in the form of a truncated FIR filter, which is suitable for postprocessing reconstructed images in digital TV, video conferencing systems, etc.

Original languageEnglish
Pages (from-to)869-877
Number of pages9
JournalSignal Processing: Image Communication
Volume15
Issue number10
DOIs
Publication statusPublished - 2000 Aug

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Blocking effect reduction of compressed images using classification-based constrained optimization'. Together they form a unique fingerprint.

  • Cite this