Local regression based colorization coding

Paul Oh, Suk Ho Lee, Moon Gi Kang

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

1 Citation (Scopus)

Abstract

A new image coding technique for color image based on colorization method is proposed. In colorization based image coding, the encoder selects the colorization coefficients according to the basis made from the luminance channel. Then, in the decoder, the chrominance channels are reconstructed by utilizing the luminance channel and the colorization coefficients sent from the encoder. The main issue in colorization based coding is to extract colorization coefficients well such that the compression rate and the quality of the reconstructed color becomes good enough. In this paper, we use a local regression method to extract the correlated feature between the luminance channel and the chrominance channels. The local regions are obtained by performing an image segmentation on the luminance channel both in the encoder and the decoder. Then, in the decoder, the chrominance values in each local region are reconstructed via a local regression method. The use of the correlated features helps to colorize the image with more details. The experimental results show that the proposed algorithm performs better than JPEG and JPEG2000 in terms of the compression rate and the PSNR value.

Original languageEnglish
Title of host publicationVISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
PublisherSciTePress
Pages153-159
Number of pages7
ISBN (Print)9789897580031
Publication statusPublished - 2014 Jan 1
Event9th International Conference on Computer Vision Theory and Applications, VISAPP 2014 - Lisbon, Portugal
Duration: 2014 Jan 52014 Jan 8

Publication series

NameVISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
Volume1

Other

Other9th International Conference on Computer Vision Theory and Applications, VISAPP 2014
CountryPortugal
CityLisbon
Period14/1/514/1/8

Fingerprint

Luminance
Image coding
Color
Image segmentation

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Oh, P., Lee, S. H., & Kang, M. G. (2014). Local regression based colorization coding. In VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (pp. 153-159). (VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications; Vol. 1). SciTePress.
Oh, Paul ; Lee, Suk Ho ; Kang, Moon Gi. / Local regression based colorization coding. VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications. SciTePress, 2014. pp. 153-159 (VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications).
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Oh, P, Lee, SH & Kang, MG 2014, Local regression based colorization coding. in VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications. VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications, vol. 1, SciTePress, pp. 153-159, 9th International Conference on Computer Vision Theory and Applications, VISAPP 2014, Lisbon, Portugal, 14/1/5.

Local regression based colorization coding. / Oh, Paul; Lee, Suk Ho; Kang, Moon Gi.

VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications. SciTePress, 2014. p. 153-159 (VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications; Vol. 1).

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

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Oh P, Lee SH, Kang MG. Local regression based colorization coding. In VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications. SciTePress. 2014. p. 153-159. (VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications).