Towards a Perceived Audiovisual Quality Model for Immersive Content

05/19/2020
by   Randy Frans Fela, et al.
0

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.

READ FULL TEXT
research
05/16/2022

Perceptual Evaluation on Audio-visual Dataset of 360 Content

To open up new possibilities to assess the multimodal perceptual quality...
research
12/22/2021

Perceptual Evaluation of 360 Audiovisual Quality and Machine Learning Predictions

In an earlier study, we gathered perceptual evaluations of the audio, vi...
research
12/03/2022

A subjective study of the perceptual acceptability of audio-video desynchronization in sports videos

This paper presents the results of a study conducted on the perceptual a...
research
01/30/2020

NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder

The development of models for quality prediction of both audio and video...
research
03/10/2021

Enhancing VMAF through New Feature Integration and Model Combination

VMAF is a machine learning based video quality assessment method, origin...
research
01/18/2019

Video Multimethod Assessment Fusion (VMAF) on 360VR contents

This paper describes the subjective experiments and subsequent analysis ...
research
09/20/2022

Subjective Assessment of High Dynamic Range Videos Under Different Ambient Conditions

High Dynamic Range (HDR) videos can represent a much greater range of br...

Please sign up or login with your details

Forgot password? Click here to reset