An Objective Quality of Experience (QoE) Assessment Index for Retargeted Images

Abstract

Content-aware image resizing (or image retargeting) is a technique that resizes images for optimum display on devices with different resolutions and aspect ratios. Traditional objective quality of experience (QoE) assessment methods are not applicable to retargeted images because the size of a retargeted image is different from its source. In this work, three determining factors for humans visual QoE on retargeted images are analyzed. They are global structural dis- tortion (G), local region distortion (L) and loss of salient information (S). Different features are selected to quantify their respective distortion degrees. Then, an objective quality assessment index, called GLS, is proposed to predict viewers' QoE by fusing selected features into one single quality score. Several regression models used for feature fusion are discussed and compared. Experimental results demonstrate that the proposed GLS quality index has stronger correlation with human QoE than other existing objective metrics in retargeted image quality assessment.