Document Type

Article

Publication Date

2015

College/Unit

Statler College of Engineering and Mining Resources

Department/Program/Center

Lane Department of Computer Science and Electrical Engineering

Abstract

The human visual system (HVS) plays an important role in stereo image quality perception. Therefore, it has aroused many people’s interest in how to take advantage of the knowledge of the visual perception in image quality assessment models. This paper proposes a full-reference metric for quality assessment of stereoscopic images based on the binocular difference channel and binocular summation channel. For a stereo pair, the binocular summation map and binocular difference map are computed first by adding and subtracting the left image and right image. Then the binocular summation is decoupled into two parts, namely additive impairments and detail losses. The quality of binocular summation is obtained as the adaptive combination of the quality of detail losses and additive impairments. The quality of binocular summation is computed by using the Contrast Sensitivity Function (CSF) and weighted multi-scale (MS-SSIM). Finally, the quality of binocular summation and binocular difference is integrated into an overall quality index. The experimental results indicate that compared with existing metrics, the proposed metric is highly consistent with the subjective quality assessment and is a robust measure. The result have also indirectly proved hypothesis of the existence of binocular summation and binocular difference channels.

Source Citation

Yang J, Lin Y, Gao Z, Lv Z, Wei W, Song H (2015) Quality Index for Stereoscopic Images by Separately Evaluating Adding and Subtracting. PLoS ONE 10(12): e0145800. https://doi.org/10.1371/journal.pone.0145800

Comments

© 2015 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

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