Do Personality and Culture Influence Perceived Video Quality and Enjoyment?

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

doi:10.1109/tmm.2016.2574623

Cited by 41 publications

(37 citation statements)

References 61 publications

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“…It has been shown that user factors can explain more of the variance in user ratings than the system factors [4], [9]. In the context of video watching experiences, social context and demographic factors [5], and personality and culture traits [6] have an impact on QoE. With respect to context, studies have looked at physical surroundings (e.g.…”

Section: B Effect Of Context and User Factors On Qoementioning

confidence: 99%

“…The method has recently been extended to integrate the encoding resolution and video size automatically [3]. Including user factor besides system factors has been used to improve the accuracy of models predicting QoE for video watching scenarios [6], [50]. To our knowledge there are no models for predicting the QoE in multi-party videoconferencing based on video quality, nor any that consider user factors and interaction for an increased accuracy.…”

Section: Prediction Of Videoconferencing Qoementioning

confidence: 99%

“…Most of these models base their estimations on an analysis of system factors only: for example encoding bitrate, or packet loss. It should be clear instead, from the definition given above, that QoE depends not only on characteristics of the videoconferencing system, but also of the context of usage and the users using it [4]- [6]. This paper sets out to understand how these elements could be integrated into a model for videoconferencing QoE estimation.…”

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confidence: 99%

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Towards Individual QoE for Multiparty Videoconferencing

Schmitt

Redi

Bulterman

et al. 2018

IEEE Trans. Multimedia

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Abstract-Video-conferencing is becoming an essential part in everyday life. The visual channel allows for interactions which were not possible over audio-only communication systems such as the telephone. However, being a de-facto over-the-top service, the quality of the delivered video-conferencing experience is subject to variations, dependent on network conditions. Video-conferencing systems adapt to network conditions by changing for example encoding bitrate of the video. For this adaptation not to hamper the benefits related to the presence of a video channel in the communication, it needs to be optimized according to a measure of the Quality of Experience (QoE) as perceived by the user. The latter is highly dependent on the ongoing interaction and individual preferences, which have hardly been investigated so far. In this paper, we focus on the impact video quality has on conversations that revolve around objects that are presented over the video channel. To this end we conducted an empirical study where groups of 4 people collaboratively build a Lego® model over a video-conferencing system. We examine the requirements for such a task by showing when the interaction, measured by visual and auditory cues, changes depending on the encoding bitrate and loss. We then explore the impact that prior experience with the technology and affective state have on QoE of participants. We use these factors to construct predictive models which double the accuracy compared to a model based on the system factors alone. We conclude with a discussion of how these factors could be applied in real world scenarios.Index Terms-Multi-Party video conferencing, Quality of Experience, Over-the-top, subjective quality, quality metrics, user study

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Section: B Effect Of Context and User Factors On Qoementioning

confidence: 99%

Section: Prediction Of Videoconferencing Qoementioning

confidence: 99%

mentioning

confidence: 99%

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Towards Individual QoE for Multiparty Videoconferencing

Schmitt

Redi

Bulterman

et al. 2018

IEEE Trans. Multimedia

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“…Consequently, they cannot be part of real-time applications such as video transmission systems. Second, their results depend on the observers' physical conditions, personality, and emotional state [26]. Therefore, the development of objective VQA methods that are able to predict the perceptual quality of visual signals is essential.…”

Section: Introductionmentioning

confidence: 99%

No-reference video quality assessment via pretrained CNN and LSTM networks

Varga

Szirányi

2019

SIViP

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A general-purpose no-reference video quality assessment algorithm based on a long short-term memory (LSTM) network and a pretrained convolutional neural network (CNN) is introduced. Considering video sequences as a time series of deep features extracted with the help of a CNN, an LSTM network is trained to predict subjective quality scores. In contrast to previous methods, the resulting algorithm was trained on the recently published Konstanz Natural Video Quality Database (KoNViD-1k), which is the only publicly available database that contains sequences with authentic distortions. The results of experiments on KoNViD-1k demonstrate that the proposed method outperforms other state-of-the-art algorithms. Furthermore, these results are also confirmed using tests on the LIVE Video Quality Assessment Database, which consists of artificially distorted videos.Keywords No-reference video quality assessment · Long short-term memory · Convolutional neural network B Domonkos Varga

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“…Hammeed et al [2] and Jang et al [6] put great efforts into converting problems of subjective video quality assessment into computational ones. Scott et al improved the computational model by considering personality and cultural influences [19]. Computational quality evaluation models have shown effectiveness in applications such as online video recommendations [1], communications [2] and video editing [20].…”

Section: Related Workmentioning

confidence: 99%

Detecting and Removing Visual Distractors for Video Aesthetic Enhancement

Zhang

et al. 2018

IEEE Trans. Multimedia

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Abstract-Personal videos often contain visual distractors, which are objects that are accidentally captured that can distract viewers from focusing on the main subjects. We propose a method to automatically detect and localize these distractors through learning from a manually labeled dataset. To achieve spatially and temporally coherent detection, we propose extracting features at the Temporal-Superpixel (TSP) level using a traditional SVM-based learning framework. We also experiment with end-to-end learning using Convolutional Neural Networks (CNNs), which achieves slightly higher performance than other methods. The classification result is further refined in a post-processing step based on graph-cut optimization. Experimental results show that our method achieves an accuracy of 81% and a recall of 86%. We demonstrate several ways of removing the detected distractors to improve the video quality, including video hole filling; video frame replacement; and camera path re-planning. The user study results show that our method can significantly improve the aesthetic quality of videos.

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Do Personality and Culture Influence Perceived Video Quality and Enjoyment?

Cited by 41 publications

References 61 publications

Towards Individual QoE for Multiparty Videoconferencing

Towards Individual QoE for Multiparty Videoconferencing

No-reference video quality assessment via pretrained CNN and LSTM networks

Detecting and Removing Visual Distractors for Video Aesthetic Enhancement

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