Color transient improvement with spatially adaptive image fusion

Joonyoung Chang, Gi Kang Moon

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

1 Citation (Scopus)

Abstract

In color TV broadcasting standards such as NTSC and PAL, the bandwidths of the chrominance signals are even narrower than those of the luminance signals. Also in digital video standards, the chrominance signals are usually low-pass filtered and sub-sampled to reduce the amount of data. Because of these reasons, the chrominance signals have poor transition characteristics and the slow transition causes blurred color edges. In this paper, a color transient improvement algorithm is proposed by exploiting the high-frequency information of the luminance signal. The high-frequency component extracted from the luminance signal is modified by adaptive gains and added to the low-resolution chrominance signals in the proposed algorithm. The gain is estimated to minimize the l2-norm of the error between the original and the estimated pixel values in a local window. The proposed algorithm naturally improves the transient of the chrominance signal as much as that of the luminance signal without overshoots and undershoots. The experimental results show that the proposed method produces steep and natural color edge transition.

Original languageEnglish
Title of host publicationProceedings of the 9th IASTED International Conference on Computer Graphics and Imaging, CGIM 2007
Pages138-143
Number of pages6
Publication statusPublished - 2007
Event9th IASTED International Conference on Computer Graphics and Imaging, CGIM 2007 - Innsbruck, Austria
Duration: 2007 Feb 132007 Feb 15

Publication series

NameProceedings of the 9th IASTED International Conference on Computer Graphics and Imaging, CGIM 2007

Other

Other9th IASTED International Conference on Computer Graphics and Imaging, CGIM 2007
CountryAustria
CityInnsbruck
Period07/2/1307/2/15

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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