  Implicit Neural Representations (INR) have recently shown to be powerful tool
for high-quality video compression. However, existing works are limiting as
they do not explicitly exploit the temporal redundancy in videos, leading to a
long encoding time. Additionally, these methods have fixed architectures which
do not scale to longer videos or higher resolutions. To address these issues,
we propose NIRVANA, which treats videos as groups of frames and fits separate
networks to each group performing patch-wise prediction. This design shares
computation within each group, in the spatial and temporal dimensions,
resulting in reduced encoding time of the video. The video representation is
modeled autoregressively, with networks fit on a current group initialized
using weights from the previous group's model. To further enhance efficiency,
we perform quantization of the network parameters during training, requiring no
post-hoc pruning or quantization. When compared with previous works on the
benchmark 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 same
compression rate. In contrast to prior video INR works which struggle with
larger resolution and longer videos, we show that our algorithm is highly
flexible and scales naturally due to its patch-wise and autoregressive designs.
Moreover, our method achieves variable bitrate compression by adapting to
videos with varying inter-frame motion. NIRVANA achieves 6X decoding speed and
scales well with more GPUs, making it practical for various deployment
scenarios.
