Iterative sparse coding for colorization based compression

Suk Ho Lee; Paul Oh; Moon Gi Kang

doi:10.1007/978-3-319-11758-4_13

Iterative sparse coding for colorization based compression

Suk Ho Lee, Paul Oh, Moon Gi Kang

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings
Editors	Mohamed Kamel, Aurélio Campilho
Publisher	Springer Verlag
Pages	112-120
Number of pages	9
ISBN (Electronic)	9783319117577
DOIs	https://doi.org/10.1007/978-3-319-11758-4_13
Publication status	Published - 2014 Jan 1
Event	11th International Conference on Image Analysis and Recognition, ICIAR 2014 - Vilamoura, Portugal Duration: 2014 Oct 22 → 2014 Oct 24

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8814
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th International Conference on Image Analysis and Recognition, ICIAR 2014
Country	Portugal
City	Vilamoura
Period	14/10/22 → 14/10/24

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All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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

Lee, Suk Ho ; Oh, Paul ; Kang, Moon Gi. / Iterative sparse coding for colorization based compression. Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings. editor / Mohamed Kamel ; Aurélio Campilho. Springer Verlag, 2014. pp. 112-120 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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{\textquoteright}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.",

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Lee, SH, Oh, P & Kang, MG 2014, Iterative sparse coding for colorization based compression. in M Kamel & A Campilho (eds), Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8814, Springer Verlag, pp. 112-120, 11th International Conference on Image Analysis and Recognition, ICIAR 2014, Vilamoura, Portugal, 14/10/22. https://doi.org/10.1007/978-3-319-11758-4_13

Iterative sparse coding for colorization based compression. / Lee, Suk Ho; Oh, Paul; Kang, Moon Gi.

Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings. ed. / Mohamed Kamel; Aurélio Campilho. Springer Verlag, 2014. p. 112-120 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8814).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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

Access to Document

10.1007/978-3-319-11758-4_13