Implicit neural representations store videos as neural networks and have performed well for
vision tasks such as video compression and denoising. With frame index and/or positional …

Implicit neural representations (INRs) recently achieved great success in image
representation and compression, offering high visual quality and fast rendering speeds with …

Implicit neural representations (INR) have gained increasing attention in representing 3D
scenes and images, and have been recently applied to encode videos (eg, NeRV, E-NeRV) …

Learning-based video compression is currently a popular research topic, offering the
potential to compete with conventional standard video codecs. In this context, Implicit Neural …

Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal
redundancies in videos. Index-based INRs ignore the content-specific spatial features and …

Encoding videos as neural networks is a recently proposed approach that allows new forms
of video processing. However, traditional techniques still outperform such neural video …

Implicit neural representation (INR) characterizes the attributes of a signal as a function of
corresponding coordinates which emerges as a sharp weapon for solving inverse problems …

Recent advances in implicit neural representation (INR)-based video coding have
demonstrated its potential to compete with both conventional and other learning-based …

Neural representation for video (NeRV), which employs a neural network to parameterize
video signals, introduces a novel methodology in video representations. However, existing …

How to represent a seismic wavefield? Traditionally, while seismic wavefields are
conceptualized continuously, acquisition geometries capture seismic data discretely using 2 …