Learned Prior Information for Image Compression

Huang Ching Chun, Phat Nguyen, Chen-Tung Lai; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


We propose a method for image compression by integrating a deep neural network (DNN) with the better portable graphics (BPG) codec. As DNN can learn the prior information from image data, it will reduce the transmission information through BPG codec and achieves a good visual quality for the decompressed image. The proposed method includes three parts: the BPG codec, the artifact reduction network and the colorization network. First, image is converted to the CIE Lab color space. Then the BPG codec compresses L component and color hint extracted from the a, b components. To satisfy the file size, the suitable QP values of BPG compression will be found for each image by binary search. Next, the decompressed L will be improved by the artifact reduction network. Finally, the colorization will predict a and b components from the decompressed L and the color hint. We evaluate the proposed method upon the Kodak image sets by the quantitative metrics (PSNR, MS-SSIM). The comparison with BPG is also presented.

Related Material


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[bibtex]
@InProceedings{Chun_2019_CVPR_Workshops,
author = {Ching Chun, Huang and Nguyen, Phat and Lai, Chen-Tung},
title = {Learned Prior Information for Image Compression},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}