Releasing the Parameter Latency of Neural Representation for High-Efficiency Video Compression

TL;DR

This paper proposes a parameter reuse scheme for implicit neural representation-based video compression, significantly improving rate-distortion performance by enhancing network parameter storage and deepening the neural network.

Contribution

It introduces a novel parameter reuse scheme and network deepening strategy to enhance INR-based video compression performance.

Findings

Significant improvement in rate-distortion performance.

Effective parameter reuse scheme enhances compression.

Deepening the network further boosts efficiency.

Abstract

For decades, video compression technology has been a prominent research area. Traditional hybrid video compression framework and end-to-end frameworks continue to explore various intra- and inter-frame reference and prediction strategies based on discrete transforms and deep learning techniques. However, the emerging implicit neural representation (INR) technique models entire videos as basic units, automatically capturing intra-frame and inter-frame correlations and obtaining promising performance. INR uses a compact neural network to store video information in network parameters, effectively eliminating spatial and temporal redundancy in the original video. However, in this paper, our exploration and verification reveal that current INR video compression methods do not fully exploit their potential to preserve information. We investigate the potential of enhancing network parameter storage through parameter reuse. By deepening the network, we designed a feasible INR parameter reuse scheme to further improve compression performance. Extensive experimental results show that our method significantly enhances the rate-distortion performance of INR video compression.