caa a22 4500 606204849 CHVBK 20210128101004.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00034-014-9840-3 doi (NATIONALLICENCE)springer-10.1007/s00034-014-9840-3 A Perceptual Image Quality Assessment Metric Using Singular Value Decomposition [Elektronische Daten] [Shuigen Wang, Dongshun Cui, Baoxian Wang, Baojun Zhao, Jinglin Yang] Image distortion can be categorized into two types: content-dependent degradation and content-independent one. Most of the existing perceptual full-reference image quality assessment (IQA) metrics cannot deal with both these two different impacts well. Singular value decomposition (SVD) as a useful mathematical tool that has been used in various image processing applications (e.g., feature extraction). In this paper, SVD is employed to decompose the images into the structural (content-dependent) and the content-independent components. For each portion, a specific assessment model is designed to tailor for its corresponding distortion properties. All the proposed models are then fused to obtain a final quality score by extreme learning machine (ELM), a machine learning technique. Extensive experimental results on six publicly available databases demonstrate that the proposed metric achieves better performance in comparison with the relevant state-of-the-art quality metrics. Springer Science+Business Media New York, 2014 Image quality assessment nationallicence Human visual system nationallicence Singular value decomposition nationallicence Extreme learning machine nationallicence Wang Shuigen School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut Cui Dongshun School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut Wang Baoxian School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut Zhao Baojun School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut Yang Jinglin School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/1(2015-01-01), 209-229 0278-081X 34:1<209 2015 34 34 https://doi.org/10.1007/s00034-014-9840-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-014-9840-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Shuigen School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 700 1- Cui Dongshun School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 700 1- Wang Baoxian School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 700 1- Zhao Baojun School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 700 1- Yang Jinglin School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/1(2015-01-01), 209-229 0278-081X 34:1<209 2015 34 34