The past few years have witnessed increasing interests in applying deep learning to video
compression. However, the existing approaches compress a video frame with only a few …

This paper proposes a Perceptual Learned Video Compression (PLVC) approach with
recurrent conditional GAN. We employ the recurrent autoencoder-based compression …

Recent years have witnessed an increasing interest in end-to-end learned video
compression. Most previous works explore temporal redundancy by detecting and …

In this paper, we propose a novel light field compression method based on a low rank-
constrained neural scene representation. While most existing methods directly compress the …

Most of the existing deep learning-based video compression frameworks rely on motion
estimation and compensation. However, the artifacts of the warped frames after motion …

Recently, learned video compression has attracted copious research attention. However,
among the existing methods, the motion used for alignment is limited to one hypothesis only …

Learned B-frame video compression aims to adopt bi-directional motion estimation and
motion compensation (MEMC) coding for middle frame reconstruction. However, previous …

Deep generative models have proven to be effective priors for solving a variety of image
processing problems. However, the learning of realistic image priors, based on a large …

The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of
standardizing the processing, coding, and transmission of video using neural networks. To …

We propose in this paper a Quantized Distilled Low-Rank Neural Radiance Field (QDLR-
NeRF) representation for the task of light field compression. While existing compression …