Pith. sign in

REVIEW

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1809.04777 v1 pith:JVH5HJXM submitted 2018-09-13 cs.HC cs.MM

classification cs.HCcs.MM
keywords signalsvideosphysiologicaltone-mappedclassificationexperiencedifferenceperceptual
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Pith tools