Stitched image quality assessment based on local measurement errors and global statistical properties

• Published in: Journal of visual communication and image representation Vol. 81; p. 103324
• Main Authors: Tian, Chongzhen, Chai, Xiongli, Shao, Feng
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2021
• Subjects: Geometric error; Image stitching; Quality aggregation; Stitched image quality assessment; Structural distortion; Stitched image quality assessment; Geometric error; Quality aggregation; Structural distortion; Image stitching
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2021.103324

Image stitching is developed to generate wide-field images or panoramic images for virtual reality applications. However, the quality assessment of stitched images with respect to various stitching algorithms has been less studied. Effective stitched image quality assessment (SIQA) is advantageous to evaluate the performance of various stitching methods and optimize the design of stitching methods. In this paper, we propose a novel SIQA method by exploiting local measurement errors and global statistical properties for feature extraction. Comprehensive image attributes including ghosting, misalignment, structural distortion, geometric error, chromatic aberrations and blur are considered either locally or globally. The extracted local and global features are aggregated into an overall quality via regression. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-the-art quality models designed for natural images and stitched images.