Visual Comfort Assessment for Stereoscopic Image Retargeting

TL;DR

This paper introduces a new database and a model for assessing visual comfort in stereoscopic image retargeting, addressing a gap in perceptual evaluation of such content.

Contribution

It presents a novel VCA metric tailored for stereoscopic retargeted images, incorporating unique features like disparity range and boundary disparity.

Findings

VCA-SIR achieves high correlation with subjective assessments

The database enables comprehensive evaluation of retargeting methods

Proposed features improve assessment accuracy

Abstract

In recent years, visual comfort assessment (VCA) for 3D/stereoscopic content has aroused extensive attention. However, much less work has been done on the perceptual evaluation of stereoscopic image retargeting. In this paper, we first build a Stereoscopic Image Retargeting Database (SIRD), which contains source images and retargeted images produced by four typical stereoscopic retargeting methods. Then, the subjective experiment is conducted to assess four aspects of visual distortion, i.e. visual comfort, image quality, depth quality and the overall quality. Furthermore, we propose a Visual Comfort Assessment metric for Stereoscopic Image Retargeting (VCA-SIR). Based on the characteristics of stereoscopic retargeted images, the proposed model introduces novel features like disparity range, boundary disparity as well as disparity intensity distribution into the assessment model. Experimental results demonstrate that VCA-SIR can achieve high consistency with subjective perception.