A Basic Multimedia Quality Model

Abstract

This paper describes two experiments designed to develop a basic multimedia predictive quality metric. In Experiment 1, two head and shoulder audio-video sequences were used for test material. Experiment 2 used one of the head and shoulder sequences from Experiment 1 together with a different, high-motion sequence. In both experiments, subjects assessed the audio quality first, followed by the video quality and finally a third test evaluated multimedia quality. The results of these studies found that human subjects integrate audio and video quality together using a multiplicative rule. A regression analysis using the subjective quality test data from each experiment found that: 1) for head and shoulder content, both modalities contribute significantly to the predictive power of the resultant model, although audio quality is weighted slightly higher than video quality and 2) for high-motion content, video quality is weighted significantly higher than audio quality.Index Terms-Multimedia, objective measurement, perceptual quality.

Service quality in P2P streaming systems

“…Similar conclusions has been made when performance degradation is caused by the codec only [113]. However, the authors in [43] highlighted importance of multiplicative component in absence of network losses. These contradictory conclusions may indicate that the model given by (4) is not the best choice for empirical modeling.…”

“…Among others, Winkler and Faller in [113] and Hands in [43] demonstrated that quality of audio information may significantly affect MOS ratings given by subjects to joint voice/video quality when two media streams are presented simultaneously. In [79] it was shown that improvements in audio quality may also result in better perception of video information even when the latter remains intact.…”

Audiovisual quality integration for interactive communications

“…However, another study from Beerends [28] based on 25-s commercials reported asymmetric interaction effects with a noticeable influence of the video quality level on the perceived audio quality (0.5 on a 5-point MOS scale) and a weaker influence of the audio quality level on the perceived video quality (only 0.15 on a MOS). Comparing these results to the ones obtained with headand-shoulders material, Hands pointed out that the nature of the audiovisual content may have influenced the results as commercials are visually more captivating, thus leading to a more video dominant situation [29].…”

Quantifying Interpretability Loss due to Image Compression

“…In exploring avenues for the development of a NIIRS-like metric for motion imagery, a clearer understanding of the factors that affect the perceived quality of motion imagery was needed (Irvine et al 2006a;Young et al 2010b). Several studies explored specific aspects of this problem, including target motion, camera motion, and frame rate, and the nature of the analysis tasks (Hands 2004;Huynh-Thu et al 2011;Moorthy et al 2010). Factors affecting perceived interpretability of motion imagery include the ground sample distance (GSD) of the imagery, motion of the targets, motion of the camera, frame rate (temporal resolution), viewing geometry, and scene complexity.…”