162 citations · 471 across the 20 of their papers we have counts for
20 papers
Transform and Bitstream Domain Image Classification
P. R. Hill, D. R. Bull
Classification of images within the compressed domain offers significant benefits. These benefits include reduced memory and computational requirements of a classification system.…
Perceptually-inspired super-resolution of compressed videos
Di Ma, Mariana Afonso, Fan Zhang +1
Spatial resolution adaptation is a technique which has often been employed in video compression to enhance coding efficiency. This approach encodes a lower resolution version of th…
An adaptive Lagrange multiplier determination method for rate-distortion optimisation in hybrid video codecs
Fan Zhang, David R. Bull
This paper describes an adaptive Lagrange multiplier determination method for rate-quality optimisation in video compression. Inspired by the experimental results of a Lagrange mul…
Quality assessment methods for perceptual video compression
Fan Zhang, David R. Bull
This paper describes a quality assessment model for perceptual video compression applications (PVM), which stimulates visual masking and distortion-artefact perception using an ada…
A Subjective Study on Videos at Various Bit Depths
Alex Mackin, Di Ma, Fan Zhang +1
Bit depth adaptation, where the bit depth of a video sequence is reduced before transmission and up-sampled during display, can potentially reduce data rates with limited impact on…
VMAF-based Bitrate Ladder Estimation for Adaptive Streaming
Angeliki V. Katsenou, Fan Zhang, Kyle Swanson +3
In HTTP Adaptive Streaming, video content is conventionally encoded by adapting its spatial resolution and quantization level to best match the prevailing network state and display…
Enhancing VMAF through New Feature Integration and Model Combination
Fan Zhang, Angeliki Katsenou, Christos Bampis +3
VMAF is a machine learning based video quality assessment method, originally designed for streaming applications, which combines multiple quality metrics and video features through…
Texture-aware Video Frame Interpolation
Duolikun Danier, David Bull
Temporal interpolation has the potential to be a powerful tool for video compression. Existing methods for frame interpolation do not discriminate between video textures and genera…
Efficient Bitrate Ladder Construction for Content-Optimized Adaptive Video Streaming
Angeliki V. Katsenou, Joel Sole, David R. Bull
One of the challenges faced by many video providers is the heterogeneity of network specifications, user requirements, and content compression performance. The universal solution o…
Study of Compression Statistics and Prediction of Rate-Distortion Curves for Video Texture
Angeliki V. Katsenou, Mariana Afonso, David R. Bull
Encoding textural content remains a challenge for current standardised video codecs. It is therefore beneficial to understand video textures in terms of both their spatio-temporal…
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…
ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation
Fan Zhang, Mariana Afonso, David R. Bull
We present a new video compression framework (ViSTRA2) which exploits adaptation of spatial resolution and effective bit depth, down-sampling these parameters at the encoder based…
DefectNET: multi-class fault detection on highly-imbalanced datasets
N. Anantrasirichai, David Bull
As a data-driven method, the performance of deep convolutional neural networks (CNN) relies heavily on training data. The prediction results of traditional networks give a bias tow…
Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
N. Anantrasirichai, Alin Achim, David Bull
This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects. In order to provide accurate detail…
Denoising Imaging Polarimetry by an Adapted BM3D Method
Alexander B. Tibbs, Ilse M. Daly, Nicholas W. Roberts +1
Imaging polarimetry allows more information to be extracted from a scene than conventional intensity or colour imaging. However, a major challenge of imaging polarimetry is image d…