High Visual-Fidelity Learned Video Compression
Meng Li Yibo Shi Jing Wang Yunqi Huang
With the growing demand for video applications, many advanced learned video
compression methods have been developed, outperforming traditional methods in
terms of objective quality metrics such as PSNR. Existing methods primarily
focus on objective quality but tend to overlook perceptual quality. Directly
incorporating perceptual loss into a learned video compression framework is
nontrivial and raises several perceptual quality issues that need to be
addressed. In this paper, we investigated these issues in learned video
compression and propose a novel High Visual-Fidelity Learned Video Compression
framework (HVFVC). Specifically, we design a novel confidence-based feature
reconstruction method to address the issue of poor reconstruction in
newly-emerged regions, which significantly improves the visual quality of the
reconstruction. Furthermore, we present a periodic compensation loss to
mitigate the checkerboard artifacts related to deconvolution operation and
optimization. Extensive experiments have shown that the proposed HVFVC achieves
excellent perceptual quality, outperforming the latest VVC standard with only
50% required bitrate.