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Implicit Neural Representations (INR) have recently shown to be powerful toolfor high-quality video compression. However, existing works are limiting asthey do not explicitly exploit the temporal redundancy in videos, leading to along encoding time. Additionally, these methods have fixed architectures whichdo not scale to longer videos or higher resolutions. To address these issues,we propose NIRVANA, which treats videos as groups of frames and fits separatenetworks to each group performing patch-wise prediction. This design sharescomputation within each group, in the spatial and temporal dimensions,resulting in reduced encoding time of the video. The video representation ismodeled autoregressively, with networks fit on a current group initializedusing weights from the previous group's model. To further enhance efficiency,we perform quantization of the network parameters during training, requiring nopost-hoc pruning or quantization. When compared with previous works on thebenchmark UVG dataset, NIRVANA improves encoding quality from 37.36 to 37.70(in terms of PSNR) and the encoding speed by 12X, while maintaining the samecompression rate. In contrast to prior video INR works which struggle withlarger resolution and longer videos, we show that our algorithm is highlyflexible and scales naturally due to its patch-wise and autoregressive designs.Moreover, our method achieves variable bitrate compression by adapting tovideos with varying inter-frame motion. NIRVANA achieves 6X decoding speed andscales well with more GPUs, making it practical for various deploymentscenarios.

NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling