162 citations · 471 across the 20 of their papers we have counts for
6 papers · 1 filter
CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement
Di Ma, Fan Zhang, David R. Bull
We propose a new Generative Adversarial Network for Compressed Video quality Enhancement (CVEGAN). The CVEGAN generator benefits from the use of a novel Mul2Res block (with multipl…
A simulation environment for drone cinematography
Fan Zhang, David Hall, Tao Xu +2
In this paper, we present a workflow for the simulation of drone operations exploiting realistic background environments constructed within Unreal Engine 4 (UE4). Methods for envir…
Video Compression with CNN-based Post Processing
Fan Zhang, Di Ma, Chen Feng +1
In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content. Among va…
Video compression with low complexity CNN-based spatial resolution adaptation
Di Ma, Fan Zhang, David R. Bull
It has recently been demonstrated that spatial resolution adaptation can be integrated within video compression to improve overall coding performance by spatially down-sampling bef…
MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering
Di Ma, Fan Zhang, David R. Bull
In this paper, we propose a novel convolutional neural network (CNN) architecture, MFRNet, for post-processing (PP) and in-loop filtering (ILF) in the context of video compression.…
BVI-DVC: A Training Database for Deep Video Compression
Di Ma, Fan Zhang, David R. Bull
Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to convention…