Fast colorization based image coding algorithm using multiple resolution images

Abstract

This paper proposes a representative pixel (RP) extraction algorithm and chrominance image recovery algorithm for the colorization-based digital image coding. The colorization-based coding methods reduce the color information of an image and achieve higher compression ratio than JPEG coding; however, they take much more computing time. In order to achieve low computational cost, this paper proposes the algorithm using the set of multiple-resolution images obtained by colorization error minimizing method. This algorithm extracts RPs from each resolution image and colorizes each resolution image utilizing a lower resolution color image, which leads to the reduction of the number of RPs and computing time. Numerical examples show that the proposed algorithm extracts the RPs and recovers the color image fast and effectively.

A colorization-based anisotropic variational model for vector-valued image compression

“…Thus, colorization-based methods mainly compress the images in the YCbCr color space, where Y is the luma component(containing rich structural details), and Cb and Cr are the chroma components(containing color information). The relationship between RGB color space and YCbCr color space is as follows: Owing to the fact that the perceived values of human eyes for the luma component and chroma component are different, the existing single-channel compression methods (such as the JPEG or JPEG2000 standard) are employed for compressing the brightness component Y in general [27,48]. For chroma channels, Cb and Cr, a small set of pixels called representative pixels in [32] are extracted from them.…”

“…In the process of decompression, colorization-based image compression methods generally use the stored image information [27,35,48], namely, the compressed brightness channel and the information of the representative pixels. First, decompress the compressed luminance channel.…”