Search Results for author: David R. Bull

Found 14 papers, 3 papers with code

ST-MFNet Mini: Knowledge Distillation-Driven Frame Interpolation

1 code implementation16 Feb 2023 Crispian Morris, Duolikun Danier, Fan Zhang, Nantheera Anantrasirichai, David R. Bull

Currently, one of the major challenges in deep learning-based video frame interpolation (VFI) is the large model sizes and high computational complexity associated with many high performance VFI approaches.

Knowledge Distillation Network Pruning +1

Perceptually-inspired super-resolution of compressed videos

no code implementations15 Jun 2021 Di Ma, Mariana Afonso, Fan Zhang, David R. Bull

Spatial resolution adaptation is a technique which has often been employed in video compression to enhance coding efficiency.

Generative Adversarial Network Super-Resolution +1

An adaptive Lagrange multiplier determination method for rate-distortion optimisation in hybrid video codecs

no code implementations15 Jun 2021 Fan Zhang, David R. Bull

This paper describes an adaptive Lagrange multiplier determination method for rate-quality optimisation in video compression.

Video Compression

Quality assessment methods for perceptual video compression

no code implementations15 Jun 2021 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 adaptive combination of noticeable distortions and blurring artefacts.

Video Compression

VMAF-based Bitrate Ladder Estimation for Adaptive Streaming

no code implementations12 Mar 2021 Angeliki V. Katsenou, Fan Zhang, Kyle Swanson, Mariana Afonso, Joel Sole, David R. Bull

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 characteristics.

Quantization

Video Compression with CNN-based Post Processing

no code implementations16 Sep 2020 Fan Zhang, Di Ma, Chen Feng, David R. Bull

In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content.

Video Compression

Video compression with low complexity CNN-based spatial resolution adaptation

no code implementations29 Jul 2020 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 before encoding and super-resolving at the decoder.

Super-Resolution Video Compression

MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering

no code implementations14 Jul 2020 Di Ma, Fan Zhang, David R. Bull

Each MFRB extracts features from multiple convolutional layers using dense connections and a multi-level residual learning structure.

Video Compression

BVI-DVC: A Training Database for Deep Video Compression

no code implementations30 Mar 2020 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 conventional approaches.

Video Compression

BVI-CC: A Dataset for Research on Video Compression and Quality Assessment

no code implementations23 Mar 2020 Angeliki V. Katsenou, Fan Zhang, Mariana Afonso, Goce Dimitrov, David R. Bull

The compression efficiency of the codecs was evaluated with commonly used objective quality metrics, and the subjective quality of their reconstructed content was also evaluated through psychophysical experiments.

Video Compression

Fast Depth Estimation for View Synthesis

1 code implementation14 Mar 2020 Nantheera Anantrasirichai, Majid Geravand, David Braendler, David R. Bull

Disparity/depth estimation from sequences of stereo images is an important element in 3D vision.

Depth Estimation

ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation

no code implementations7 Nov 2019 Fan Zhang, Mariana Afonso, David R. Bull

Our results show consistent and significant compression gains against HM and VVC based on Bj{\o}negaard Delta measurements, with average BD-rate savings of 12. 6% (PSNR) and 19. 5% (VMAF) over HM and 5. 5% (PSNR) and 8. 6% (VMAF) over VTM.

Video Compression

Denoising Imaging Polarimetry by an Adapted BM3D Method

no code implementations13 Nov 2017 Alexander B. Tibbs, Ilse M. Daly, Nicholas W. Roberts, David R. Bull

Imaging polarimetry allows more information to be extracted from a scene than conventional intensity or colour imaging.

Denoising

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