Exploring the acceptability of the audiovisual quality for a mobile video session based on objectively measured parameters.
ABSTRACT Although Quality of Experience (QoE) plays a major role in the design and development of mobile applications and services, QoE assessment is still challenging, especially in real-life (so called 'living lab') contexts. This paper presents results from an exploratory, interdisciplinary study on QoE and more specifically, on the acceptability of varying audiovisual qualities during video watching on a mobile device, in a realistic setting. The subjectively measured acceptability is modeled by means of a decision tree which uses information about the network type, transport protocol, technical video characteristics, and user behavior. The proposed model may contribute to the estimation of users' subjective evaluation of the quality during mobile video watching and to QoE optimization by dynamically altering the parameters that have the largest influence on this subjective evaluation.
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ABSTRACT: Quality of experience (QoE) measures the overall perceived quality of mobile video delivery from subjective user experience and objective system performance. Current QoE prediction models have two main limitations: (1) insufficient consideration of the factors influencing QoE, and (2) limited studies on QoE models for acceptability prediction. In this paper, a set of novel acceptability-based QoE models, denoted as A-QoE, is proposed based on the results of comprehensive user studies on subjective quality acceptance assessments. The models are able to predict users’ acceptability and pleasantness in various mobile video usage scenarios. Statistical nonlinear regression analysis has been used to build the models with a group of influencing factors as independent predictors, which include encoding parameters and bitrate, video content characteristics, and mobile device display resolution. The performance of the proposed A-QoE models has been compared with three well-known objective Video Quality Assessment metrics: PSNR, SSIM and VQM. The proposed A-QoE models have high prediction accuracy and usage flexibility. Future user-centred mobile video delivery systems can benefit from applying the proposed QoE-based management to optimize video coding and quality delivery strategies.IEEE Transactions on Multimedia 04/2014; 16(3):738 - 750. DOI:10.1109/TMM.2014.2298217 · 1.78 Impact Factor