Authors:
Maria Grazia Albanesi
and
Riccardo Amadeo
Affiliation:
University of Pavia, Italy
Keyword(s):
Video Quality Evaluation, Eye Tracking, No Reference Objective Metric.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Visual Attention and Image Saliency
Abstract:
In this paper, we present an innovative algorithm based on a voting process approach, to analyse the data provided by an eye tracker during tasks of user evaluation of video quality. The algorithm relies on the hypothesis that a lower quality video is more “challenging” for the Human Visual System (HVS) than a high quality one, and therefore visual impairments influence the user viewing strategy. The goal is to generate a map of saliency of the human gaze on video signals, in order to create a No Reference objective video quality assessment metric. We consider the impairment of video compression (H.264/AVC algorithm) to generate different versions of video quality. We propose a protocol that assigns different playlists to different user groups, in order to avoid any effect of memorization of the visual stimuli on strategy. We applied our algorithm to data generated on a heterogeneous set of video clips, and the final result is the computation of statistical measures which provide a r
ank of the videos according to the perceived quality. Experimental results show that there is a strong correlation between the metric we propose and the quality of impaired video, and this fact confirms the initial hypothesis.
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