Deep Contextual Video Compression

TL;DR

This paper introduces a deep contextual video compression framework that shifts from traditional predictive coding to conditional coding using feature domain context, significantly improving compression efficiency.

Contribution

It proposes a novel deep contextual coding approach leveraging feature domain context, enabling higher quality reconstruction and outperforming previous state-of-the-art methods.

Findings

Achieves 26.0% bitrate savings over x265 veryslow preset for 1080P videos.

Outperforms previous deep video compression methods in experiments.

Framework is flexible and extensible for various conditions.

Abstract

Most of the existing neural video compression methods adopt the predictive coding framework, which first generates the predicted frame and then encodes its residue with the current frame. However, as for compression ratio, predictive coding is only a sub-optimal solution as it uses simple subtraction operation to remove the redundancy across frames. In this paper, we propose a deep contextual video compression framework to enable a paradigm shift from predictive coding to conditional coding. In particular, we try to answer the following questions: how to define, use, and learn condition under a deep video compression framework. To tap the potential of conditional coding, we propose using feature domain context as condition. This enables us to leverage the high dimension context to carry rich information to both the encoder and the decoder, which helps reconstruct the high-frequency contents for higher video quality. Our framework is also extensible, in which the condition can be flexibly designed. Experiments show that our method can significantly outperform the previous state-of-the-art (SOTA) deep video compression methods. When compared with x265 using veryslow preset, we can achieve 26.0% bitrate saving for 1080P standard test videos.