Objective quality assessment of retargeted images based on RBF neural network with structural distortion and content change

• Published in: Multimedia tools and applications Vol. 82; no. 5; pp. 7463 - 7477
• Main Authors: Zhou, Bin, Liu, Ze’an, Ji, Jiayu, Wang, Xuanyin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2023; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Distortion; Evaluation; Feature extraction; Histograms; Image quality; Multimedia Information Systems; Neural networks; Patches (structures); Quality assessment; Radial basis function; Special Purpose and Application-Based Systems; Feature matching; RBF neural network; Visual saliency; Image retargeting quality assessment
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-13662-w

Objective quality assessment of retargeted images aims to find the best retargeting method for showing an image on different display terminals. This paper uses a Radial Basis Function (RBF) neural network to assess the quality of retargeted images. First, invariant feature points in the original image and their counterparts in the retargeted image are matched in a spatial order-preserving manner. Feature points centered local patches are extracted from original and retargeted images. Then, multi-scale local structural similarity and multi-scale local HSV color histograms difference of matched local patches are measured. A saliency map is employed as the weights of the local patches for evaluating the structural distortion and content change. Finally, the overall assessment of the retargeted image quality can be obtained by the RBF neural network. Experimental results on a benchmark test show the high consistency between the proposed objective assessment and subjective evaluations, and our method is closer to the practical application due to its simplicity compared with those complex ones.