Do Personality and Culture Influence Perceived Video Quality and Enjoyment?

Scott, Michael ORCID: https://orcid.org/0000-0002-6803-1490, Guntuku, Sharath Chandra, Lin, Weisi and Ghinea, Gheorghita (2016) Do Personality and Culture Influence Perceived Video Quality and Enjoyment? IEEE Transactions on Multimedia, 18 (9). pp. 1796-1807. ISSN 1520-9210

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Abstract / Summary

The interplay between system, context and human factors is important in perception of multimedia quality. However, studies on human factors are very limited in comparison to those for system and context factors. This article presents an attempt to explore the influence of personality and cultural traits on perception of multimedia quality. As a first step, a database consisting of 144 video sequences from 12 short movie excerpts has been assembled and rated by 114 participants from a cross-cultural population. Thereby providing a useful ground-truth for this (as well as future) study. As a second step, three statistical models are compared: (i) a baseline model to only consider system factors; (ii) an extended model to include personality and culture; and (iii) an optimistic model in which each participant is modeled. As a third step, predictive models based on content, affect, system, and human factors are trained to generalize the statistical findings. As shown by statistical analysis, personality and cultural traits represent 9.3% of the variance attributable to human factors and human factors overall predict an equal or higher proportion of variance compared to system factors. Moreover, the quality-enjoyment correlation varies across the excerpts. Predictive models trained by including human factors demonstrate about 3% and 9% improvement over models trained solely based on system factors for predicting perceived quality and enjoyment. As evidenced by this, human factors indeed are important in perceptual multimedia quality, but the results suggest further investigation of moderation effects and a broader range of human factors is necessary.

Item Type:	Article
Identification Number:	10.1109/TMM.2016.2574623
ISSN:	1520-9210
Subjects:	Computing & Data Science Social Sciences Communication
Courses by Department:	The School of Film & Television
Depositing User:	Michael Scott
Date Deposited:	28 Apr 2016 15:28
Last Modified:	18 Nov 2024 14:02
URI:	https://repository.falmouth.ac.uk/id/eprint/1716

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