How to Design and Train Your Implicit Neural Representation for Video Compression

TL;DR

This paper introduces RNeRV, a state-of-the-art implicit neural video compression method that balances quality, size, and training speed, and explores hyper-networks for real-time encoding.

Contribution

It develops a comprehensive review of NeRV-based methods, proposes RNeRV with improved performance, and investigates hyper-networks for faster, real-time video compression.

Findings

RNeRV outperforms existing methods with +1.27% PSNR at equal training time.

Hyper-networks enable real-time encoding with 1.7% PSNR and MS-SSIM improvements.

Masking INR weights enhances compression quality at similar bitrates.

Abstract

Implicit neural representation (INR) methods for video compression have recently achieved visual quality and compression ratios that are competitive with traditional pipelines. However, due to the need for per-sample network training, the encoding speeds of these methods are too slow for practical adoption. We develop a library to allow us to disentangle and review the components of methods from the NeRV family, reframing their performance in terms of not only size-quality trade-offs, but also impacts on training time. We uncover principles for effective video INR design and propose a state-of-the-art configuration of these components, Rabbit NeRV (RNeRV). When all methods are given equal training time (equivalent to 300 NeRV epochs) for 7 different UVG videos at 1080p, RNeRV achieves +1.27% PSNR on average compared to the best-performing alternative for each video in our NeRV library. We then tackle the encoding speed issue head-on by investigating the viability of hyper-networks, which predict INR weights from video inputs, to disentangle training from encoding to allow for real-time encoding. We propose masking the weights of the predicted INR during training to allow for variable, higher quality compression, resulting in 1.7% improvements to both PSNR and MS-SSIM at 0.037 bpp on the UCF-101 dataset, and we increase hyper-network parameters by 0.4% for 2.5%/2.7% improvements to PSNR/MS-SSIM with equal bpp and similar speeds. Our project website is available at https://mgwillia.github.io/vinrb/ and our code is available at https://github.com/mgwillia/vinrb.