Iterative sparse coding for colorization based compression

Suk Ho Lee, Paul Oh, Moon Gi Kang

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

2 Citations (Scopus)

Abstract

Colorization based coding is a technique which compresses a color image using the colorization method. The main issue in colorization based coding is to extract a good RP(representative pixel) set from the original color image from which the colored image can be reconstructed in the decoder to a sufficient level. In this paper, we propose an iterative sparse coding method for the extraction of the RP set. Observations show that the proposed method computes simultaneously the locally optimal RP set and the locally optimal Levin’s colorization matrix. Furthermore, experimental results show that the proposed method provides better color image reconstruction and compression rate than conventional colorization based coding methods.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings
EditorsMohamed Kamel, Aurélio Campilho
PublisherSpringer Verlag
Pages112-120
Number of pages9
ISBN (Electronic)9783319117577
DOIs
Publication statusPublished - 2014
Event11th International Conference on Image Analysis and Recognition, ICIAR 2014 - Vilamoura, Portugal
Duration: 2014 Oct 222014 Oct 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8814
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Image Analysis and Recognition, ICIAR 2014
CountryPortugal
CityVilamoura
Period14/10/2214/10/24

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lee, S. H., Oh, P., & Kang, M. G. (2014). Iterative sparse coding for colorization based compression. In M. Kamel, & A. Campilho (Eds.), Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings (pp. 112-120). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8814). Springer Verlag. https://doi.org/10.1007/978-3-319-11758-4_13