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

Implicit neural representations (INRs) have emerged as a promising approach for video
storage and processing showing remarkable versatility across various video tasks. However …

Implicit neural representations for video (NeRV) have recently become a novel way for high-
quality video representation. However existing works employ a single network to represent …

In Implicit Neural Representations (INRs) a discrete signal is parameterized by a neural
network that maps coordinates to the signal samples. INRs were successfully employed for …

With the increasing consumption of 3D displays and virtual reality, multi-view video has
become a promising format. However, its high resolution and multi-camera shooting result in …

Despite the abundant availability and content richness for video data, its high-dimensionality
poses challenges for video research. Recent advancements have explored the implicit …

In this paper, we propose a neural representation for videos that enables real-time quality-
scalable decoding, called QS-NeRV. QS-NeRV comprises a Self-Learning Distribution …

Abstract Implicit Neural Networks (INRs) have emerged as powerful representations to
encode all forms of data, including images, videos, audios, and scenes. With video, many …

Digital terrain models (DTMs) are pivotal in remote sensing cartography and landscape
management requiring accurate surface representation and topological information …