An Efficient Content-aware Downsampling-based Video Compression Framework
Hao Jiang, Li Chen · 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) · 2022
Recently, deep learning-based video compression algorithms have achieved competitive performance in Bjøntegaard delta (BD) rate, especially those adopting super-resolution networks as post-processing modules in downsampling-based video compression (DBC) frameworks. However, limited by the non-differentiable characteristics of traditional codecs, DBC frameworks mainly focus on improving the performance of super-resolution modules while ignoring optimizing downscaling modules. It is crucial to improve video compression performance without introducing additional modifications to the decoder client in practical application scenarios. We propose a context-aware processing network (CPN) compatible with standard codecs with no computational burden introduced to the client, which preserves the critical information and essential structures during downscaling. The proposed CPN works as a precoder cascaded by standard codecs to improve the compression performance on the server before encoding and transmission. Besides, a surrogate codec is employed to simulate the degradation process of the standard codecs and backpropagate the gradient to optimize the CPN. Experimental results show that the proposed method outperforms latest pre-processing networks and achieves considerable performance compared with the latest DBC frameworks.