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

Abstract

Quality of Experience (QoE) assessment of multimedia services is a challenging task and an understanding of how the user perceives quality at the physiological level would facilitate this. 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 paper investigated the relationships between changes in EEG features and subjective quality test scores (i.e. MOS) for High Dynamic Range (HDR) images viewed with a mobile device. Results show that changes in the gamma and beta bands correlated negatively with MOS, whereas positive correlations were observed in the alpha band. Coupling between activities in the delta and beta bands (i.e. positive correlation between power in the fast beta and slow delta frequency bands) is related to anxiety and dissatisfaction. Thus, the results suggest that increases in the degree of coupling are associated with decreases in HDR quality. This also suggests that in the HDR image QoE assessment, human emotions play a significant role. Potentially, these findings may be exploited in objective QoE perception modelling.

Premium Hdr: The Impact of a Singleword on the Quality of Experience of Hdr Video

“…Depending on the methodology, subjective tests may provide an immense amount of useful information regarding the personal qual-ity preferences and the specific perceptual thresholds of the test participant; however, opinion scores do not report anything about the internal physiological levels of the individual. The work of Al-juboori et al [11] uses electroencephalogram (EEG) to analyze the correlation between the perceived quality of HDR images and the different bands of brain activity. The 4 TMOs were applied to the 5 source HDR images and the 20 stimuli were shown to the 28 test participants on an iPhone 6.…”

Video Quality Measurement For Buffering Time Based On EEG Frequency Feature

“…The author of [7] also pointed out that some traditional video quality assessment models cannot yet evaluate video buffering problems. In addition, with the development of cranial nerve signal monitoring instruments, the authors of [8,9] used the EEG method to evaluate QoE of speech system and images, and obtained more accurate results. Aiming at the problem of video buffering, from the user's point of view, while addressing the shortcomings of users' subjective ratings being greatly influenced by users, in this paper we use the EEG method to accurately quantify and evaluate user QoE under different levels of video buffering time, especially within 1000ms, to better achieve a higher QoE under limited network resources.…”