Multiple-level feature-based measure for retargeted image quality

• Main Authors: Zhang, Yabin, Lin, Weisi, Li, Qiaohong, Cheng, Wentao, Zhang, Xinfeng
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Retargeted Image Quality; Edge Group Similarity
• Online Access: https://hdl.handle.net/10356/142323
• Institution: Nanyang Technological University

Objective image retargeting quality assessment aims to use computational models to predict the retargeted image quality consistent with subjective perception. In this paper, we propose a multiple-level feature (MLF)-based quality measure to predict the perceptual quality of retargeted images. We first provide an in-depth analysis on the low-level aspect ratio similarity feature, and then propose a mid-level edge group similarity feature, to better address the shape/structure related distortion. Furthermore, a high-level face block similarity feature is designed to deal with sensitive region deformation. The multiple-level features are complementary as they quantify different aspects of quality degradation in the retargeted image, and the MLF measure learned by regression is used to predict the perceptual quality of retargeted images. Extensive experimental results performed on two public benchmark databases demonstrate that the proposed MLF measure achieves higher quality prediction accuracy than the existing relevant state-of-the-art quality measures.