The CP-QAE-I: A video dataset for exploring the effect of personality and culture on perceived quality and affect in multimedia

Guntuku, Sharath Chandra; Scott, Michael James; Yang, Huan; Ghinea, Gheorghiţă; Wang, Lin

doi:10.1109/qomex.2015.7148130

Cited by 15 publications

(7 citation statements)

References 29 publications

(29 reference statements)

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“…This dataset has been used in particular in the MediaEval benchmarking initiative to compare computational models estimating the emotions elicited by movies [85]. Based on the FilmStim dataset [81], Guntuku et al proposed the CP-QAE-I dataset to explore the effect of quality of experience, personality and culture on the felt emotions [86]. This dataset is composed of 144 videos, with various bit-rate, frame dimension, and frame rate, derived from 12 original video excerpts.…”

Section: Affective Video Datasetsmentioning

confidence: 99%

Affective Video Content Analysis: A Multidisciplinary Insight

Baveye

Chamaret

Dellandréa

et al. 2018

IEEE Trans. Affective Comput.

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International audienceIn our present society, the cinema has become one of the major forms of entertainment providing unlimited contexts of emotion elicitation for the emotional needs of human beings. Since emotions are universal and shape all aspects of our interpersonal and intellectual experience, they have proved to be a highly multidisciplinary research field, ranging from psychology, sociology, neuroscience, etc., to computer science. However, affective multimedia content analysis work from the computer science community benefits but little from the progress achieved in other research fields. In this paper, a multidisciplinary state-of-the-art for affective movie content analysis is given, in order to promote and encourage exchanges between researchers from a very wide range of fields. In contrast to other state-of-the-art papers on affective video content analysis, this work confronts the ideas and models of psychology, sociology, neuroscience, and computer science. The concepts of aesthetic emotions and emotion induction, as well as the different representations of emotions are introduced, based on psychological and sociological theories. Previous global and continuous affective video content analysis work, including video emotion recognition and violence detection, are also presented in order to point out the limitations of affective video content analysis work

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Section: Affective Video Datasetsmentioning

confidence: 99%

Affective Video Content Analysis: A Multidisciplinary Insight

Baveye

Chamaret

Dellandréa

et al. 2018

IEEE Trans. Affective Comput.

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“…The CP-QAE-I video dataset (available from http://1drv.ms/1M1bnwU) was validated in a previous study [22], and the study presented in this article represents its first full-scale application. The video dataset contains sequences based on 12 excerpts from popular movies that were selected purposively to evoke different affects [57].…”

Section: Video Datasetmentioning

confidence: 99%

Do Personality and Culture Influence Perceived Video Quality and Enjoyment?

Scott

Guntuku

Wang

et al. 2016

IEEE Trans. Multimedia

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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.

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“…The challenge lies in correctly identifying and understanding the key elements dominating the vast space of context and user factors. QoE studies have already revealed differences between tasks [4] and user preferences [5], social context [6], personality [7] and more [1].…”

Section: Introductionmentioning

confidence: 99%

Instrumental Assessment of Near-end Perceived Listening Effort

Schmitt¹,

Redi²,

Garcia³

2016

5th ISCA/DEGA Workshop on Perceptual Quality of Systems (PQS 2016)

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In modern video conferencing services, just as in common video delivery, most of the resource optimization is taken care of in the codec layer. Modern codecs like H.264 use detailed perceptual models to optimize the data reduction in way that it is least noticed by us. Already early evaluations of telecommunication systems could establish that there are different thresholds for a good quality depending on the situation. It is further known that subjective quality perceptions vary from user to user. But the space of user and context factors is still largely unexplored. To gain insight in which parameters are key in differentiating quality perception, we need to explore the interaction in different situations while keeping a tight control over the system parameters. In this paper we explore how clustering participants by their interaction or rating behavior can reveal subgroups that show significantly different perception of the QoE delivered by the same videoconferencing system. While for a cluster of users we find video quality to influence other QoE dimensions such as audio, for another cluster this is not the case. We explore whether this effect is due to conversational dynamics (contextual factor) or individual preferences (user factor) and discuss what this would mean for the design of future video-conferencing systems, that want to dynamically adapt to situation and participants.

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The CP-QAE-I: A video dataset for exploring the effect of personality and culture on perceived quality and affect in multimedia

Cited by 15 publications

References 29 publications

Affective Video Content Analysis: A Multidisciplinary Insight

Affective Video Content Analysis: A Multidisciplinary Insight

Do Personality and Culture Influence Perceived Video Quality and Enjoyment?

Instrumental Assessment of Near-end Perceived Listening Effort

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