Towards a Perceived Audiovisual Quality Model for Immersive Content

TL;DR

This study investigates audiovisual quality assessment for immersive 360 videos and ambisonic audio, proposing models that correlate objective metrics with subjective perceptions to enhance predictive accuracy.

Contribution

It introduces a new audiovisual quality model for immersive content, emphasizing the importance of synchronized high-quality databases and improved subjective testing methodologies.

Findings

Cross-Format SPSNR-NN correlates better with subjective video scores.

A power model achieves high correlation with audiovisual test data.

Enhanced assessor training improves discrimination of multichannel audio quality.

Abstract

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.