Author: Aaron

I received the B.Sc. (Hons) and M.Sc. degrees from Shanghai Jiao Tong University, Shanghai, China, in 2005 and 2008 respectively, and the Ph.D degree from University of Bristol, Bristol, United Kingdom, in 2012. I am currently working in the Visual Information Laboratory, which is led by Prof. David Bull, within the Department of Electrical and Electronic Engineering , University of Bristol, as a Research Assistant, with projects on parametric video coding and Immersive Technology Lab.

BVI-SR: A Study of Subjective Video Quality at Various Spatial Resolutions

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Alex Mackin, Mariana Afonso, Fan Zhang, and David Bull

ABSTRACT

BVI-SR contains 24 unique video sequences at a range of spatial resolutions up to UHD-1 (3840p). These sequences were used as the basis for a large-scale subjective experiment exploring the relationship between visual quality and spatial resolution when using three distinct spatial adaptation filters (including a CNN-based super-resolution method). The results demonstrate that while spatial resolution has a significant impact on mean opinion scores (MOS), no significant reduction in visual quality between UHD-1 and HD resolutions for the superresolution method is reported. A selection of image quality metrics were benchmarked on the subjective evaluations, and analysis indicates that VIF offers the best performance.

SOURCE SEQUENCES

DATABASE DOWNLOAD

[DOWNLOAD] subjective data, instructions and related file.

[DOWNLOAD] all videos from University of Bristol Research Data Storage Facility.

[DOWNLOAD] all videos from MS OneDrive. Please fill a simple registration form to get access. The MS OneDrive verification code will be sent within up to 2 days after we receive the form. Please note the code may be in your Spam box.

 

If this content has been mentioned in a research publication, please give credit to the University of Bristol, by referencing the following paper:

[1] A. Mackin, M. Afonso, F. Zhang and D. Bull, “A study of subjective video quality at various spatial resolutions”, IEEE ICIP, 2018.

[2] A. Mackin, M. Afonso, F. Zhang and D. Bull,”BVI-SR Database“, 2020.

BVI-DVC: A Training Database for Deep Video Compression

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Di Ma, Fan Zhang and David Bull

INTRODUCTION

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. Such approaches often employ Convolutional Neural Networks (CNNs) which are trained on databases with relatively limited content coverage. In this work, a new extensive and representative video database, BVI-DVC is presented for training CNN-based video compression systems, with specific emphasis on machine learning tools that enhance conventional coding architectures, including spatial resolution and bit depth up-sampling, post-processing and in-loop filtering. BVI-DVC contains 800 sequences at various spatial resolutions from 270p to 2160p and has been evaluated on ten existing network architectures for four different coding tools. Experimental results show that this database produces significant improvements in terms of coding gains over three existing (commonly used) image/video training databases under the same training and evaluation configurations. The overall additional coding improvements by using the proposed database for all tested coding modules and CNN architectures are up to 10.3% based on the assessment of PSNR and 8.1% based on VMAF.

SOURCE EXAMPLES

PERFORMANCE

This database has been compared to other three commonly used datasets for training ten popular network architectures which are employed in four different CNN-based coding modules (in the context of HEVC). The figure below shows the average coding gains in terms of BD-rates on JVET test sequences over original HEVC.

USEFUL LINKS

[DOWNLOAD] all videos of this database.

[README] before using the database and for copyright permissions.

If there is any issue regarding this database, please contact fan.zhang@bristol.ac.uk

REFERENCE

If this content has been mentioned in a research publication, please give credit to the University of Bristol, by referencing:

[1] Di Ma, Fan Zhang and David Bull, “BVI-DVC: A Training Database for Deep Video Compression“, arXiv:2003.13552, 2020.

[2] Di Ma, Fan Zhang and David Bull,”BVI-DVC“, 2020.

Comparing VVC, HEVC and AV1 using Objective and Subjective Assessments

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Fan Zhang, Angeliki Katsenou, Mariana Afonso, Goce Dimitrov and David Bull

ABSTRACT

In this paper, the performance of three state-of-the-art video codecs: High Efficiency Video Coding (HEVC) Test Model (HM), AOMedia Video 1 (AV1) and Versatile Video Coding Test Model (VTM), are evaluated using both objective and subjective quality assessments. Nine source sequences were carefully selected to offer both diversity and representativeness, and different resolution versions were encoded by all three codecs at pre-defined target bitrates. The compression efficiency of the three codecs are evaluated using two commonly used objective quality metrics, PSNR and VMAF. The subjective quality of their reconstructed content is also evaluated through psychophysical experiments. Furthermore, HEVC and AV1 are compared within a dynamic optimization framework (convex hull rate-distortion optimization) across resolutions with a wider bitrate, using both objective and subjective evaluations. Finally the computational complexities of three tested codecs are compared. The subjective assessments indicate that, for the tested versions there is no significant difference between AV1 and HM, while the tested VTM version shows significant enhancements. The selected source sequences, compressed video content and associated subjective data are available online, offering a resource for compression performance evaluation and objective video quality assessment.

Parts of this work have been presented in the IEEE International Conference on Image Processing (ICIP) 2019 in Taipei and in the Alliance for Open Media (AOM) Symposium 2019 in San Francisco.

SOURCE SEQUENCES

DATABASE

[DOWNLOAD] subjective data.

[DOWNLOAD] all videos from University of Bristol Research Data Storage Facility.

If this content has been mentioned in a research publication, please give credit to the University of Bristol, by referencing the following paper:

[1] A. V. Katsenou, F. Zhang, M. Afonso and D. R. Bull, “A Subjective Comparison of AV1 and HEVC for Adaptive Video Streaming,” 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 4145-4149.

[2] F. Zhang, A. V. Katsenou, M. Afonso, Goce Dimitrov and D. R. Bull, “Comparing VVC, HEVC and AV1 using Objective and Subjective Assessments”, arXiv:2003. 10282 [eess.IV], 2020.

ViSTRA: Video Compression based on Resolution Adaptation

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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 criteria, and up-sampling at the decoder using a deep convolution neural network. ViSTRA2 has been integrated with the reference software of both the HEVC (HM 16.20) and VVC (VTM 4.01), and evaluated under the Joint Video Exploration Team Common Test Conditions using the Random Access configuration. Our results show consistent and significant compression gains against HM and VVC based on Bjonegaard 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.

PROPOSED ALGORITHM

RESULTS

BD-rate results of ViSTRA2 when HM 16.20 was employed as host codec.

BD-rate results of ViSTRA2 when VTM 4.01 was employed as host codec.

REFERENCE

[1] F. Zhang, M. Afonso and D. R. Bull, ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation. arXiv preprint arXiv:1911.02833.

[2] M. Afonso, F. Zhang and D. R. Bull, Video Compression based on Spatio-Temporal Resolution Adaptation. IEEE T-CSVT (Letter), 2019.

[3] M. Afonso, F. Zhang, A. Katsenou, D. Agrafiotis, D. Bull, Low Complexity Video Coding Based on Spatial Resolution Adaptation, ICIP, 2017.

Rate-distortion Optimization Using Adaptive Lagrange Multipliers

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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. This originated some 20 years ago based on an entropy-constrained high-rate approximation and experimental results obtained using an H.263 reference encoder on limited test material. In this work, we conducted a comprehensive analysis of the results of a Lagrange multiplier selection experiment conducted on various video content using H.264/AVC and HEVC reference encoders. These results show that the original Lagrange multiplier selection methods, employed in both video encoders, are able to achieve optimum rate-distortion performance for I and P frames, but fail to perform well for B frames. The relationship is identified between the optimum Lagrange multipliers for B frames and distortion information obtained from the experimental results, leading to a novel Lagrange multiplier determination approach. The proposed method adaptively predicts the optimum Lagrange multiplier for B frames based on the distortion statistics of recent reconstructed frames. After integration into both H.264/AVC and HEVC reference encoders, this approach was evaluated on 36 test sequences with various resolutions and differing content types. The results show consistent bitrate savings for various hierarchical B frame configurations with minimal additional complexity. BD savings average approximately 3% when constant QP values are used for all frames, and 0.5\% when non-zero QP offset values are employed for different B frame hierarchical levels.

 

REFERENCE

[1] Fan Zhang and David, R. Bull, “Rate-distortion Optimization Using Adaptive Lagrange Multipliers”, IEEE Trans. on CSVT, accepted in 2018.

[2] F. Zhang and D. Bull, “An Adaptive Lagrange Multiplier Determination Method for Rate-distortion Optimisation in Hybrid Video Codecs”. IEEE ICIP, 2015.