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Under the limited storage, computing and network bandwidth resources, thevideo compression coding technology plays an important role for visualcommunication. To efficiently compress raw video data, a colorization-basedvideo compression coding method is proposed in this paper. In the proposedencoder, only the video luminance components are encoded and transmitted. Torestore the video chrominance information, a generative adversarial network(GAN) model is adopted in the proposed decoder. In order to make the GAN workefficiently for video colorization, the generator of the proposed GAN modeladopts an optimized MultiResUNet, an attention module, and a mixed lossfunction. Experimental results show that when compared with the H.265/HEVCvideo compression coding standard using all-intra coding structure, theproposed video compression coding method achieves an average of 72.05% BDBRreduction, and an average of 4.758 dB BDPSNR increase. Moreover, to ourknowledge, this is the first work which compresses videos by using GAN-basedcolorization, and it provides a new way for addressing the video compressioncoding problems.

Video Compression Coding via Colorization: A Generative Adversarial Network (GAN)-Based Approach