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Improvement of ColorizationBased Coding Using Optimization by Novel Colorization Matrix Construction and Adaptive Color Conversion
Kazu MISHIBA Takeshi YOSHITOME
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E98D
No.11
pp.19431949 Publication Date: 2015/11/01
Online ISSN: 17451361
DOI: 10.1587/transinf.2015EDP7106
Type of Manuscript: PAPER Category: Image Processing and Video Processing Keyword: colorizationbased coding, image compression, colorization matrix, edgepreserving filtering, adaptive color conversion,
Full Text: PDF>>
Summary:
This study improves the compression efficiency of Lee's colorizationbased coding framework by introducing a novel colorization matrix construction and an adaptive color conversion. Colorizationbased coding methods reconstruct color components in the decoder by colorization, which adds color to a base component (a grayscale image) using scant color information. The colorization process can be expressed as a linear combination of a few column vectors of a colorization matrix. Thus it is important for colorizationbased coding to make a colorization matrix whose column vectors effectively approximate color components. To make a colorization matrix, Lee's colorizationbased coding framework first obtains a base and color components by RGBYCbCr color conversion, and then performs a segmentation method on the base component. Finally, the entries of a colorization matrix are created using the segmentation results. To improve compression efficiency on this framework, we construct a colorization matrix based on a correlation of basecolor components. Furthermore, we embed an edgepreserving smoothing filtering process into the colorization matrix to reduce artifacts. To achieve more improvement, our method uses adaptive color conversion instead of RGBYCbCr color conversion. Our proposed color conversion maximizes the sum of the local variance of a base component, which resulted in increment of the difference of intensities at region boundaries. Since segmentation methods partition images based on the difference, our adaptive color conversion leads to better segmentation results. Experiments showed that our method has higher compression efficiency compared with the conventional method.

