Video Quality Measurement For Buffering Time Based On Eeg Frequency Feature

Currently, user-based quality of experience (QoE) measurement methods (e.g., mean opinion score, MOS) are often employed. However, their results might be affected by human subjective experience and thoughts. Physiological measurement methods can overcome these disadvantages. In the field of video quality models, the video buffering problem caused by poor network conditions is an important factor that affects QoE. In this paper, a reasonable physiological measurement method, electroencephalography (EEG), is proposed to quantitatively analyze QoE changes when users face different levels of video buffering time. By extracting the band power of EEG signals as the feature and analyzing the correlation and variance, a more objective buffering time EEG (BT-EEG) score model of video quality under the single-factor video buffering time is established, which solves the problem of uneven subjective data quality.