PFR-VC: Learning-Based Video Compression Framework with Predicted Frame Refinement

Learning-based video compression has attracted more and more attention in recent years. Traditional video coding relies on block-based motion estimation and spatial frequency transformation. While these techniques can effectively compress videos, further enhancing the compression ratio becomes challenging. Introducing deep learning methods can overcome the limitations of manually designed algorithms. In this paper, we propose a learning-based video compression framework with Predicted Frame Refinement (PFR) to improve the compression efficiency. Firstly, a simple autoencoder is introduced to encode the motion information, eliminating the need for a complex optical-flow network. Then, we design a predicted frame refinement network with an attention feature fusion mechanism to generate predicted frames more suitable for extracting context. Finally, we introduce a context coding scheme to improve the compression ratio by jointly utilizing temporal prior and hyper prior. The entire network can be globally optimized and trained from scratch. The experimental result shows that the proposed compression framework outperforms previous methods. Our approach brings 31.2% more saved bit rate than x265 with veryslow preset. Our model also achieves a 7.1% gain in Multi-Scale Structural Similarity Index Measure (MS-SSIM) compared with the recent method proposed by Guo et al.(2023).