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Modeling the relationships between changes in EEG features and subjective quality of HDR images

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Advances in Telemedicine for Health Monitoring: Technologies, Design and Applications — Recommend this title to your library

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Quality of experience (QoE) is a human-centric paradigm, which produces the blueprint of human-behavioral-states such as perception, emotion, cognition, and expectation. Recent advances in neurophysiological monitoring tools have facilitated the study of frequency, time, and location of neuronal activity to an unprecedented degree, as well as opened doors to a better understanding of human overall behavioral systems. Physiological signals, such as the electroencephalogram (EEG), have shown promise in revealing the subject's emotion or attention in quality assessment and the correlation of this with media service quality. This chapter proposes a novel objective QoE model for high dynamic range (HDR) images and is based on the relationship between objective (i.e. delta-beta coupling) and subjective measures (i.e. mean opinion score MOS). The analysis of the results indicate that the proposed QoE model has a strong correlation with MOS scores, hence can be effectively used in predicting the overall HDR image quality. An advantage of the model is that it is lightweight and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization. Future mobile providers can benefit from applying the proposed QoE-based model to optimize users' acceptability and satisfaction for different HDR image scenarios.

Chapter Contents:

• 11.1 Introduction
• 11.2 Related work
• 11.3 Dataset generation
• 11.3.1 Tone-mapping operators
• 11.3.2 Test stimuli
• 11.3.3 Participants
• 11.3.4 Test setup
• 11.3.5 Test methodology
• 11.3.6 Preprocessing
• 11.3.7 Feature extraction
• 11.4 EEG signal acquisition
• 11.5 Analysis of results
• 11.5.1 Subjective rating analysis
• 11.5.2 EEG signal analysis
• 11.5.3 Correlation and analysis of variance
• 11.5.4 The coupling measurements
• 11.6 A mobile EEG-based QoE model
• 11.6.1 EEG-based QoE model based on regression technique
• 11.6.2 Model evaluation
• 11.7 Limitations
• 11.7.1 Experimental set-up
• 11.7.2 Limitations using mobile devices
• 11.7.3 Limitations using the EEG device
• 11.8 Summary
• References

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