AUTHOR=Katsenou Angeliki , Zhang Fan , Afonso Mariana , Dimitrov Goce , Bull  David R. 

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

JOURNAL=Frontiers in Signal Processing

VOLUME=Volume 2 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/signal-processing/articles/10.3389/frsip.2022.874200

DOI=10.3389/frsip.2022.874200

ISSN=2673-8198

ABSTRACT=The video technology scenery has been very vivid over the past years, with novel video coding technologies introduced that promise improved compression performance over state-of-the-art technologies. This paper features a codec comparison dataset, the BVI-CC, produced with the purpose of a performance assessment of three competitive video codecs: High Efficiency Video Coding (HEVC) Test Model (HM), AOMedia Video 1 (AV1), and Versatile Video Coding Test Model (VTM). Nine source video sequences were carefully selected to offer both diversity and representativeness in the spatio-temporal domain. Different spatial resolution versions of the sequences were created and encoded by all three codecs at pre-defined target bit rates. The compression efficiency of the codecs was evaluated with commonly used objective quality metrics. Furthermore, the subjective quality of their reconstructed content was evaluated through psychophysical experiments. Additionally, HEVC and AV1 were compared within an adaptive bit rate framework (convex hull rate-distortion optimization across spatial resolutions), using both objective and subjective evaluations. Finally, the computational complexities of the tested codecs were examined. The findings from 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. All data have been made publicly available as part of the dataset.