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About Us

The Visual Information Laboratory of the University of Bristol exists to undertake innovative, collaborative and interdisciplinary research resulting in world leading technology in the areas of computer vision, image and video communications, content analysis and distributed sensor systems. VI-Lab was formed in 2010, merging the two well established research groups, Signal Processing (EEEng) and Computer Vision (CS). The two constituent groups offer shared and complementary strengths and, with a history of successful collaboration since 1993, their merger has created one of the largest groupings of its type in the UK.

ICME2020 Grand Challenge: Encoding in the Dark

Sponsors The awards will be sponsored by Facebook and Netflix.   Low light scenes often come with acquisition noise, which not only disturbs the viewers, but it also makes video compression challenging. These types of…

Evaluating Video Codecs Through Objective and Subjective Assessments

We have evaluated the performance of two state-of-the-art video codecs, High Efficiency Video Coding (HEVC) Test Model (HM) and AOMedia Video 1 (AV1) through objective and subjective quality assessments. Nine source sequences were carefully selected…

Evaluating viewing experience of drone videos

Stephen Boyle, Fan Zhang and David Bull This page will contain a download link to the test content and validation results for the paper accepted by IEEE ICIP 2019.

ViSTRA: Video Compression based on Resolution Adaptation

Fan Zhang, Mariana Afonso and David Bull ABSTRACT 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 on perceptual…

Rate-distortion Optimization Using Adaptive Lagrange Multipliers

Fan Zhang and David Bull ABSTRACT This page introduces the work of rate-distortion optimisation using adaptive Lagrange Multipliers. In current standardized hybrid video encoders, the Lagrange multiplier determination model is a key component in rate-distortion optimization….

FRQM: A Frame Rate Dependent Video Quality Metric

Fan Zhang, Alex Mackin and David Bull ABSTRACT This page introduces the work of an objective quality metric (FRQM), which characterises the relationship between variations in frame rate and perceptual video quality. The proposed method…

BVI-SR Video Database

This page will contain a download link to the BVI-SR (Spatial Resolution) video database if accepted into ICIP 2018  

VI-Lab PhD Research Opportunities

Funded Opportunities VI-Lab currently has fully funded PhD opportunities in the following areas: _________________________________________________________________________ Title: Using optical coherence imaging to determine visual prognosis in neurological disease   Supervisor: Professor Alin Achim and Dr Denize Atan (Bristol…


The MultiDrone Consortium organized the first Drone Cinematography Workshop at the University of Bristol on 6 December 2017. The aim of the workshop was to bring together experts in drone cinematography – users, producers, and…

BVISS: Pattern Recognition without Features or Training

Professor Fred Stentiford – UCL Pattern recognition is usually implemented through the use of a selected set of plausible features that characterise the data being studied. In addition it is also necessary to identify a…

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