Towards a Perceived Audiovisual Quality Model for Immersive Content

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This paper studies the quality of multimedia content focusing on 360 video and ambisonic spatial audio reproduced using a head-mounted display and a multichannel loudspeaker setup. Encoding parameters following basic video quality test conditions for 360 videos were selected and a low-bitrate codec was used for the audio encoder. Three subjective experiments were performed for the audio, video, and audiovisual respectively. Peak signal-to-noise ratio (PSNR) and its variants for 360 videos were computed to obtain objective quality metrics and subsequently correlated with the subjective video scores. This study shows that a Cross-Format SPSNR-NN has a slightly higher linear and monotonic correlation over all video sequences. Based on the audiovisual model, a power model shows a highest correlation between test data and predicted scores. We concluded that to enable the development of superior predictive model, a high quality, critical, synchronized audiovisual database is required. Furthermore, comprehensive assessor training may be beneficial prior to the testing to improve the assessors' discrimination ability particularly with respect to multichannel audio reproduction. In order to further improve the performance of audiovisual quality models for immersive content, in addition to developing broader and critical audiovisual databases, the subjective testing methodology needs to be evolved to provide greater resolution and robustness.

Original languageEnglish
Title of host publicationProceedings of 12th International Conference on Quality of Multimedia Experience
Number of pages9
Publication dateMay 2020
Article number9123134
ISBN (Electronic)9781728159652
Publication statusPublished - May 2020
Event12th International Conference on Quality of Multimedia Experience - Athlone, Ireland
Duration: 26 May 202028 May 2020


Conference12th International Conference on Quality of Multimedia Experience


  • 360 video
  • Ambisonics
  • Audiovisual quality assessment
  • Design of experiment
  • Perceptual evaluation
  • PSNR


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