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

TL;DR

This paper introduces a novel GAN-based video compression method that encodes only luminance data and reconstructs chrominance via colorization, significantly reducing bitrate while maintaining quality.

Contribution

It presents the first use of GAN-based colorization for video compression, combining optimized neural network components for efficient chrominance reconstruction.

Findings

Achieves 72.05% BDBR reduction compared to H.265/HEVC.

Increases BDPSNR by 4.758 dB on average.

First work to use GAN-based colorization for video compression.

Abstract

Under the limited storage, computing and network bandwidth resources, the video compression coding technology plays an important role for visual communication. To efficiently compress raw video data, a colorization-based video compression coding method is proposed in this paper. In the proposed encoder, only the video luminance components are encoded and transmitted. To restore the video chrominance information, a generative adversarial network (GAN) model is adopted in the proposed decoder. In order to make the GAN work efficiently for video colorization, the generator of the proposed GAN model adopts an optimized MultiResUNet, an attention module, and a mixed loss function. Experimental results show that when compared with the H.265/HEVC video compression coding standard using all-intra coding structure, the proposed video compression coding method achieves an average of 72.05% BDBR reduction, and an average of 4.758 dB BDPSNR increase. Moreover, to our knowledge, this is the first work which compresses videos by using GAN-based colorization, and it provides a new way for addressing the video compression coding problems.