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Adaptive Block Matching Based Quantization for Lossy Image Compression

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 8th European Workshop on Visual Information Processing (EUVIP)
PublisherIEEE
Pages4-9
Number of pages6
ISBN (Electronic)978-1-7281-4496-2
ISBN (Print)978-1-7281-4497-9
DOIs
Publication statusPublished - Oct 2019
Publication typeA4 Article in a conference publication
EventEuropean Workshop on Visual Information Processing -
Duration: 1 Jan 1900 → …

Publication series

NameEuropean Workshop on Visual Information Processing
ISSN (Print)2164-974X
ISSN (Electronic)2471-8963

Conference

ConferenceEuropean Workshop on Visual Information Processing
Period1/01/00 → …

Abstract

We propose an efficient method of quantization of discrete cosine transform (DCT) coefficients for lossy image compression. The main novelty of our approach is in utilization of masking ability of image regions having large values of error of block matching (searching patches similar to a given one). We propose to calculate a quantization level for a given image region in a proportion to a block matching error for this region, and describe the method of additional quantization of DCT coefficients for JPEG compression. We collect the mean opinion scores for a designed image test set containing compressed images for three different quantization schemes, and show by numerical analysis of 300 test images ofTAMPERE17 database, that the proposed method for the same compression ratio is able to provide significantly better MOS value, than one based on the conventional quantization. We also show that for different fixed values of the CSSIM4 metric, the proposed quantization provides a compression ratio increase by 30%-50% comparing to the conventional quantization.

Keywords

  • lossy image compression, quantization, group sparsity, self-similarity, human visual system, full reference image visual quality assessment, block matching

Publication forum classification

Field of science, Statistics Finland