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 …

Implicit neural representations (INRs) were recently proposed as a new video compression
paradigm with existing approaches performing on par with HEVC. However such methods …

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 …

Numerous studies have recently advanced the state-of-the art for representing videos
through an implicit neural network (INR). As these models become increasingly ubiquitous …

Neural fields, mapping low-dimensional input coordinates to corresponding signals, have
shown promising results in representing various signals. Numerous methodologies have …

Implicit neural representations (INR) excel in encoding videos within neural networks,
showcasing promise in computer vision tasks like video compression and denoising. INR …

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

Learning-based Neural Video Codecs (NVCs) have emerged as a compelling alternative to
the standard video codecs, demonstrating promising performance, and simple and easily …

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 …

Neural fields, also known as implicit neural representations (INRs), have shown a
remarkable capability of representing, generating, and manipulating various data types …