Comparing VVC, HEVC and AV1 using Objective and Subjective Assessments

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.

[1]  Yoann Baveye,et al.  AccAnn: A New Subjective Assessment Methodology for Measuring Acceptability and Annoyance of Quality of Experience , 2019, IEEE Transactions on Multimedia.

[2]  Touradj Ebrahimi,et al.  Subjective Quality Evaluation via Paired Comparison: Application to Scalable Video Coding , 2011, IEEE Transactions on Multimedia.

[3]  Fan Zhang,et al.  ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation , 2019, ArXiv.

[4]  Gustavo de Veciana,et al.  An information fidelity criterion for image quality assessment using natural scene statistics , 2005, IEEE Transactions on Image Processing.

[5]  Sheila S. Hemami,et al.  VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images , 2007, IEEE Transactions on Image Processing.

[6]  G. Bjontegaard,et al.  Calculation of Average PSNR Differences between RD-curves , 2001 .

[7]  K. R. Rao,et al.  High efficiency video coding , 2016, 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).

[8]  Itu-T and Iso Iec Jtc Advanced video coding for generic audiovisual services , 2010 .

[9]  Humberto de Jesús Ochoa Domínguez,et al.  Versatile Video Coding , 2019 .

[10]  Cisco Visual Networking Index: Forecast and Methodology 2016-2021.(2017) http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual- networking-index-vni/complete-white-paper-c11-481360.html. High Efficiency Video Coding (HEVC) Algorithms and Architectures https://jvet.hhi.fraunhofer. , 2017 .

[11]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[12]  Saverio G. Blasi,et al.  AN OVERVIEW OF RECENT VIDEO CODING DEVELOPMENTS IN MPEG AND AOMEDIA , 2018 .

[13]  Touradj Ebrahimi,et al.  Comparison of Compression Efficiency between HEVC/H.265, VP9 and AV1 based on Subjective Quality Assessments , 2018, 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX).

[14]  ITU-T Rec. P.910 (04/2008) Subjective video quality assessment methods for multimedia applications , 2009 .

[15]  Touradj Ebrahimi,et al.  Calculation of average coding efficiency based on subjective quality scores , 2014, Journal of Visual Communication and Image Representation.

[16]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[17]  Angeliki V. Katsenou,et al.  A Subjective Comparison of AV1 and HEVC for Adaptive Video Streaming , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[18]  Fan Zhang,et al.  A Perception-Based Hybrid Model for Video Quality Assessment , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[20]  Stefan Winkler,et al.  Analysis of Public Image and Video Databases for Quality Assessment , 2012, IEEE Journal of Selected Topics in Signal Processing.

[21]  Fan Zhang,et al.  Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments , 2011, IEEE Transactions on Multimedia.

[22]  Fan Zhang,et al.  BVI-HD: A Video Quality Database for HEVC Compressed and Texture Synthesized Content , 2018, IEEE Transactions on Multimedia.

[23]  Detlev Marpe,et al.  Coding efficiency comparison of AV1/VP9, H.265/MPEG-HEVC, and H.264/MPEG-AVC encoders , 2016, 2016 Picture Coding Symposium (PCS).

[24]  Jens-Rainer Ohm Multimedia Signal Coding and Transmission , 2015 .

[25]  Fan Zhang,et al.  Support for reduced presentation durations in subjective video quality assessment , 2016, Signal Process. Image Commun..

[26]  Mariana Afonso,et al.  Video Compression Based on Spatio-Temporal Resolution Adaptation , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[27]  Angeliki V. Katsenou,et al.  BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Quality Assessment , 2021, IEEE Transactions on Multimedia.

[28]  Ke Wang,et al.  On the Optimal Presentation Duration for Subjective Video Quality Assessment , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[29]  Ioannis Katsavounidis,et al.  Video codec comparison using the dynamic optimizer framework , 2018, Optical Engineering + Applications.

[30]  Andrey Norkin,et al.  Video Codec Testing and Quality Measurement , 2019 .

[31]  Manish Narwaria,et al.  Data Analysis in Multimedia Quality Assessment: Revisiting the Statistical Tests , 2017, IEEE Transactions on Multimedia.

[32]  C. Duchon Lanczos Filtering in One and Two Dimensions , 1979 .

[33]  Jan De Cock,et al.  Compression Performance Comparison of x264, x265, libvpx and aomenc for On-Demand Adaptive Streaming Applications , 2018, 2018 Picture Coding Symposium (PCS).

[34]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[35]  Christian Timmerer,et al.  A Practical Evaluation of Video Codecs for Large-Scale HTTP Adaptive Streaming Services , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[36]  Fan Zhang,et al.  A Parametric Framework for Video Compression Using Region-Based Texture Models , 2011, IEEE Journal of Selected Topics in Signal Processing.