论文标题
通过循环框架预测推进学到的视频压缩
Advancing Learned Video Compression with In-loop Frame Prediction
论文作者
论文摘要
近年来，人们对端到端学习的视频压缩产生了越来越多的兴趣。大多数以前的作品通过检测和压缩运动图来探索时间冗余，以将参考框架转换为目标框架。然而，它未能充分利用顺序参考框架中的历史先验。在本文中，我们使用环内框架预测模块提出了一种先进的学习视频压缩方法（ALVC）方法，该模块能够从先前压缩的框架中有效预测目标框架，而不会消耗任何位率。预测的框架可以比先前压缩的框架更好，因此有益于压缩性能。提出的环内预测模块是端到端视频压缩的一部分，并在整个框架中共同优化。我们分别提出了用于压缩P框架和B框架的复发和双向内部预测模块。实验显示了我们ALVC方法在学习的视频压缩中的最新性能。我们还以PSNR的范围优于X265的默认层次B模式，并在MS-SSIM上击败SSIM调整的X265的最慢模式。项目页面：https：//github.com/renyang-home/alvc。
Recent years have witnessed an increasing interest in end-to-end learned video compression. Most previous works explore temporal redundancy by detecting and compressing a motion map to warp the reference frame towards the target frame. Yet, it failed to adequately take advantage of the historical priors in the sequential reference frames. In this paper, we propose an Advanced Learned Video Compression (ALVC) approach with the in-loop frame prediction module, which is able to effectively predict the target frame from the previously compressed frames, without consuming any bit-rate. The predicted frame can serve as a better reference than the previously compressed frame, and therefore it benefits the compression performance. The proposed in-loop prediction module is a part of the end-to-end video compression and is jointly optimized in the whole framework. We propose the recurrent and the bi-directional in-loop prediction modules for compressing P-frames and B-frames, respectively. The experiments show the state-of-the-art performance of our ALVC approach in learned video compression. We also outperform the default hierarchical B mode of x265 in terms of PSNR and beat the slowest mode of the SSIM-tuned x265 on MS-SSIM. The project page: https://github.com/RenYang-home/ALVC.
