Learned Video Compression with Adaptive Temporal Prior and Decoded Motion-aided Quality Enhancement

Learned video compression has drawn great attention and shown promising compression performance recently. In this paper, we focus on the two components in learned video compression framework, i.e., conditional entropy model and quality enhancement module, to improve compression performance. Specifically, we propose an adaptive spatial-temporal entropy model for image, motion and residual compression, which introduces temporal prior to reduce temporal redundancy of latents and an additional modulated mask to evaluate the similarity and perform refinement. Besides, a quality enhancement module is proposed for predicted frame and reconstructed frame to improve frame quality and reduce bitrate cost of residual coding. The module reuses decoded optical flow as motion prior and utilizes deformable convolution to mine high-quality information from reference frame in a bit-free manner. The two proposed coding tools are integrated into a pixel-domain residual-coding based compression framework to evaluate their effectiveness. Experimental results demonstrate that our framework achieves competitive compression performance in low-delay scenario, compared with recent learning-based methods and traditional H.265/HEVC in terms of PSNR and MS-SSIM. The code is available at OpenLVC.