Blind image quality assessment utilising local mean eigenvalues

Jiachen Yang; Kyohoon Sim; Bin Jiang; Wen Lu

Blind image quality assessment utilising local mean eigenvalues

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Author(s): Jiachen Yang¹ ; Kyohoon Sim¹ ; Bin Jiang¹ ; Wen Lu²
- Affiliations: 1: School of Electrical and Information Engineering, Tianjin University , Tianjin , People's Republic of China ;
  2: School of Electronic Engineering, Xidian University , Xian , People's Republic of China
Source: Volume 54, Issue 12, 14 June 2018, p. 754 – 756
DOI: 10.1049/el.2018.0958 , Print ISSN 0013-5194, Online ISSN 1350-911X

Received 20/03/2018, Published 04/05/2018

Eigenvalues are intrinsic and representative values of a square matrix. They have thus been used in many image processing areas due to their important application value, but not in the image quality assessment (IQA) field. In this Letter, the authors study the correlation between local mean eigenvalues (LMEs) and perceptual quality of images, and demonstrate the applicability of LMEs in IQA. The LMEs are related to structural complexity of images. The LMEs and natural scene statistics features are utilised for a sparse dictionary learning. Experimental results conducted on promising IQA databases show their method's superiority in comparison with top-performing blind IQA metrics.

References

1. 1)
  - 1. Moorthy, A.K., Bovik, A.C.: ‘A two-step framework for constructing blind image quality indices’, Signal Process. Lett., 2010, 17, (5), pp. 513–516 (doi: 10.1109/LSP.2010.2043888).
2. 2)
  - 9. Wu, Q., Li, H., Wang, Z., et al: ‘Blind image quality assessment based on rank-order regularized regression’, Trans. Multimed., 2017, PP, (99), p. 1.
3. 3)
  - 7. Xue, W., Zhang, L., Mou, X.: ‘Learning without human scores for blind image quality assessment’, Comput. Vision Pattern Recognit. IEEE, 2013, 9, pp. 995–1002.
4. 4)
  - 8. Mittal, A., Moorthy, A., Bovik, A.: ‘No-reference image quality assessment in the spatial domain’, IEEE Trans. Image Process., 2012, 21, (12), pp. 4695–4708 (doi: 10.1109/TIP.2012.2214050).
5. 5)
  - 5. Doermann, D.: ‘Unsupervised feature learning framework for no-reference image quality assessment’. IEEE Conf. Computer Vision and Pattern Recognition, IEEE Computer Society, Providence, RI, USA, June 2012, vol. 157, pp. 1098–1105.
6. 6)
  - 2. Moorthy, A.K., Bovik, A.C.: ‘Blind image quality assessment: from natural scene statistics to perceptual quality’, Trans. Image Process., 2011, 20, (12), p. 3350 (doi: 10.1109/TIP.2011.2147325).
7. 7)
  - 6. Mittal, A., Soundararajan, R., Bovik, A.C.: ‘Making a completely blind image quality analyzer’, Signal Process. Lett., 2013, 20, (3), pp. 209–212 (doi: 10.1109/LSP.2012.2227726).
8. 8)
  - 3. Saad, M.A., Bovik, A.C., Charrier, C.: ‘Blind image quality assessment: a natural scene statistics approach in the DCT domain’, Trans. Image Process., 2012, 21, (8), p. 3339 (doi: 10.1109/TIP.2012.2191563).
9. 9)
  - 12. Ponomarenko, N., Ieremeiev, O., Lukin, V., et al: ‘Color image database TID2013: peculiarities and preliminary results’. European Workshop on Visual Information Processing, IEEE, Paris, France, June 2013, pp. 106–111.
10. 10)
  - 10. He, L., Tao, D., Li, X., et al: ‘Sparse representation for blind image quality assessment’. IEEE Conf. Computer Vision and Pattern Recognition, IEEE Computer Society, Providence, RI, USA, June 2012, vol. 23, pp. 1146–1153.
11. 11)
  - 8. Sang, Q., Wu, X., Li, C., et al: ‘Blind image quality assessment using a reciprocal singular value curve’, Image Commun., 2014, 29, (10), pp. 1149–1157.
12. 12)
  - 11. Larson, E.C., Chandler, D.M.: ‘Most apparent distortion: full-reference image quality assessment and the role of strategy’, J. Electron. Imaging, 2010, 19, (1), p. 011006 (doi: 10.1117/1.3267105).

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Blind image quality assessment utilising local mean eigenvalues

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