Browse > Article
http://dx.doi.org/10.5909/JBE.2018.23.2.227

Objective Quality Assessment Method for Stitched Images  

Billah, Meer Sadeq (Seoul National University of Science and Technology, Department of Electrical and Information Engineering)
Ahn, Heejune (Seoul National University of Science and Technology, Department of Electrical and Information Engineering)
Publication Information
Journal of Broadcast Engineering / v.23, no.2, 2018 , pp. 227-234 More about this Journal
Abstract
Recently, stitching techniques are used for obtaining wide FOV, e.g., panorama contents, from normal cameras. Despite many proposed algorithms, the no objective quality evaluation method is developed, so the comparison of algorithms are performed only in subjective way. The paper proposes a 'Delaunay-triangulation based objective assessment method' for evaluating the geometric and photometric distortions of stitched or warped images. The reference and target images are segmented by Delaunay-triangulation based on matched points between two images, the average Euclidian distance is used for geometric distortion measure, and the average or histogram of PSNR for photometric measure. We shows preliminary results with several test images and stitching methods for demonstrate the benefits and application.
Keywords
Quality assessment; objective measure; image stitching; panorama; reference based;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Wu, Y. "test Image for Image Stitching, available on https://github.com/ppwwyyxx/OpenPano
2 V. Pham, "test images for Image stitching, available on https://github.com/phvu/misc/tree/master/imageStitch
3 Delaunay, Boris. "Sur la sphere vide." Izv. Akad. Nauk SSSR, Otdelenie Matematicheskii i Estestvennyka Nauk 7.793-800 (1934): 1-2.
4 P. Topiwala, W. Dai, M. Krishnan, A. Abbas, A. S. Doshi, D. Newman, "Performance comparison of AV1, HEVC, and JVET video codecs on 360 (spherical) video," Applications of Digital Image Processing XL, Vol. 10396, p. 1039609, September, 2017.
5 R. Szeliski, "Image alignment and stitching: A tutorial. Foundations and Trends," Computer Graphics and Vision, Vol 2, No. 1, pp. 1-104. 2006.   DOI
6 Brown, M., & Lowe, D. G. (2007). Automatic panoramic image stitching using invariant features. International journal of computer vision, 74(1), 59-73.   DOI
7 R. Hartley, A Zisserman, "Multiple View Geometry in Computer Vision," 2nd Ed., Cambridge Press, New York, NY, USA. February, 2003.
8 F. Dornaika, R. Chung, "Mosaicking images with parallax," Signal Processing: Image Communication, Vol. 19, No. 8, pp. 771-786, 2004.   DOI
9 A. Eden, M. Uyttendaele, R. Szeliski, "Seamless image stitching of scenes with large motions and exposure differences. In Computer Vision and Pattern Recognition, New York, NY, USA, pp. 2498-2505. 2006.
10 J. Zaragoza, T. J. Chin, M. S. Brown, D. Suter, "As-projective-as-possible image stitching with moving DLT," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, USA, pp. 2339-2346, 2013
11 C. H. Chang, Y. Sato, Y. Y. Chuang. "Shape-preserving half-projective warps for image stitching," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, USA pp. 3254-3261, 2014.
12 Qureshi, H. S., Khan, M. M., Hafiz, R., Cho, Y., & Cha, J. (2012). Quantitative quality assessment of stitched panoramic images. IET Image Processing, 6(9), 1348-1358.   DOI
13 D. Ghosh, N. Kaabouch, "A survey on image mosaicing techniques," Journal of Visual Communication and Image Representation, Vol. 34, No.1, pp. 1-11, January, 2016.   DOI