Perceptual video quality evaluation by means of physiological signals

The proliferation of multimedia technology and its wide adoption by users has created the need for more effective metrics for Quality of Experience (QoE). Objective video quality metrics usually under-perform in terms of perceptual quality, thus evaluation is usually performed offline by people, an arduous and time consuming task that is also affected by external conditions and by user preferences. The use of physiological signals, recorded from users exposed to multimedia stimuli, has the potential to offer a more robust and unbiased method for evaluating perceptual quality. In this work, we propose the evaluation of the perceptual quality of video by means of cerebral (Electroencephalography — EEG) and peripheral (Electrocardiography — ECG and Electromyography — EMG) physiological signals. A machine learning approach is employed in order to map features extracted from these signals to a subjective video quality scale. Five 4K video sequences were encoded at different quality levels using the state-of-the-art HEVC codec and their quality was evaluated by real users while recording their physiological signals. The quality levels decided by the proposed model were then evaluated against the user-provided MOSs and the results demonstrated the potential of the proposed method for accurate perceptual video quality evaluation.