Combining Frame and GOP Embeddings for Neural Video Representation

Implicit neural representations (INRs) were recently proposed as a new video compression paradigm with existing approaches performing on par with HEVC. However such methods only perform well in limited settings e.g. specific model sizes fixed aspect ratios and low-motion videos. We address this issue by proposing T-NeRV a hybrid video INR that combines frame-specific embeddings with GOP-specific features providing a lever for content-specific fine-tuning. We employ entropy-constrained training to jointly optimize our model for rate and distortion and demonstrate that T-NeRV can thereby automatically adjust this lever during training effectively fine-tuning itself to the target content. We evaluate T-NeRV on the UVG dataset where it achieves state-of-the-art results on the video representation task outperforming previous works by up to 3dB PSNR on challenging high-motion sequences. Further our method improves on the compression performance of previous methods and is the first video INR to outperform HEVC on all UVG sequences.