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This paper proposes a new color image coding scheme called “colorization image coding.” The scheme is based on the colorization technique which can colorize a monochrome image by giving a small number of color pixels. We develop algorithms useful for color image coding. First, the luminance component is separated from an input color image. Then, a small number of color seeds are selected as chrominance information. The luminance image component is coded by a lossy coding technique and the chrominance image component is stored as color seeds. The decoding is performed by the colorization algorithm. It is shown that this colorization technique is effective to image coding, especially for high compression rate, through the experiments using different types of images.we develop a new strategy that attempts to account for the higher-level context of each pixel. The colorizations generated by our approach exhibit a much higher degree of spatial consistency, compared to previous automatic color transfer methods. We also demonstrate that our method requires considerably less manual effort than previous user-assisted colorizationmethods Given a grayscale image to colorize, we first determine for each pixel which example segment it should learn itscolor from. This is done automatically using a robust supervised classification scheme that analyzes the low-level feature space defined by small neighborhoods of pixels in the example image. Next, each pixel is assigned a colorfrom the appropriate region using a neighborhood matching metric, combined with spatial filtering for improved spatial coherence. Each color assignment is associated with a confidence value, and pixels with a sufficiently high confidence level are provided as “micro-scribbles” to the optimization-based colorization algorithm of Levin et al., which produces the final complete colorization of the image.

Ambika Sharma, Abhinash Singla and Rajdavinder Singh Boparai

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