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A Novel No-reference Subjective Quality Metric for Free Viewpoint Video Using Human Eye Movement

conference contribution
posted on 2022-11-23, 23:32 authored by PK Podder, M Paul, M Murshed
The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants eye-tracker recorded spatio-temporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.

History

Volume

10749 LNCS

Pagination

237-251

Location

Wuhan, PEOPLES R CHINA

Start date

2017-11-20

End date

2017-11-24

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319757858

Language

English

Editor/Contributor(s)

Huang Q

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event

8th Pacific-Rim Symposium on Image and Video Technology (PSIVT)

Publisher

SPRINGER INTERNATIONAL PUBLISHING AG

Series

Lecture Notes in Computer Science