Image Segmentation Based GraphCut Approach to Fast Color Image Coding via Graph Fourier Transform Colorizationbased image coding is a technique to compress chrominance information of an image using a colorization technique. The conventional algorithm applies graph Fourier transform to the colorizationbased coding. In this algorithm, several pixels on the image are defined as vertices of the graph, and the chrominance values of that pixels are set as graph signals. Then, the graph signal corresponding to the several chrominance values on the image is transformed to the graph spectrum based on the graph Fourier transform, and the graph spectrum is compressed and stored. Because the stored graph spectrum gives the graph signal on the image based on the inverse graph Fourier transform in decoding phase, the color image is recovered from the luminance image and the several chrominance values corresponding to the graph signal. However, high calculation time is required to perform graph Fourier transform, and therefore, this paper proposes a fast graph Fourier transform to improve the conventional colorizationbased image coding algorithm. In numerical examples, although the PSNR value is decreased 0.3 dB, the proposed algorithm is 16.8 times faster than the conventional method.

Color Image Compression with Transform Domain DownSampling and Deep Convolutional Reconstruction In this paper, we build up a new blockbased color image compression scheme based on our proposed transform domain downsampling method and deep convolutional reconstruction algorithm. Specifically, our proposed downsampling scheme aims to downsample each N xc3x97 N transform block into the N2 xc3x97 N2 block for the saving of bitcost. On the other hand, the proposed deep convolutional reconstruction algorithm is employed to reconstruct the downsampled block for a full resolution reconstruction. We apply our proposed methods to both the chrominance components to compress color images. Experimental results show that our proposed method achieves excellent results when used in practice.