Perceptual Experience Analysis for Tone-mapped HDR Videos based on EEG and Peripheral Physiological Signals

TL;DR

This study investigates perceptual experiences of tone-mapped HDR videos using subjective questionnaires and physiological signals like EEG, revealing that HDR videos are more engaging and natural, with physiological differences correlating to perceived quality.

Contribution

It introduces a combined subjective and physiological approach to analyze perceptual differences in HDR videos, highlighting effective EEG features for classification.

Findings

HDR videos are perceived as more interesting and natural.

Physiological signals differ significantly between HDR and LDR videos.

Gamma band features are effective for classifying perceptual differences.

Abstract

High dynamic range (HDR) imaging has been attracting much attention as a technology that can provide immersive experience. Its ultimate goal is to provide better quality of experience (QoE) via enhanced contrast. In this paper, we analyze perceptual experience of tone-mapped HDR videos both explicitly by conducting a subjective questionnaire assessment and implicitly by using EEG and peripheral physiological signals. From the results of the subjective assessment, it is revealed that tone-mapped HDR videos are more interesting and more natural, and give better quality than low dynamic range (LDR) videos. Physiological signals were recorded during watching tone-mapped HDR and LDR videos, and classification systems are constructed to explore perceptual difference captured by the physiological signals. Significant difference in the physiological signals is observed between tone-mapped HDR and LDR videos in the classification under both a subject-dependent and a subject-independent scenarios. Also, significant difference in the signals between high versus low perceived contrast and overall quality is detected via classification under the subject-dependent scenario. Moreover, it is shown that features extracted from the gamma frequency band are effective for classification.