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A hybrid indicator for realistic blurred image quality assessment
Affiliation:1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China;2. Department of Computer Science and Engineering, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, PR China;3. Business Intelligence and Visualization Research Center, National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai, 200436, PR China;4. Shanghai Engineering Research Center of Big Data & Internet Audience, Shanghai, 200072, PR China;5. Innovation College North-Chiang Mai University, 169 Moo3, Nong Kaew, Hang Dong, Chiang Mai 50230 Thailand;6. International College of Digital Innovation, Chiang Mai University, Chiang Mai, 50200, Thailand;1. Department of Electronics and Communication, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab 144011, India;2. Department of Computer Science and Engg., UIET, Sector 25, Panjab University, Chandigarh 160023, India;1. College of Information Engineering, Shanghai Maritime University, Shanghai 200135, China;2. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;3. School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Computer Networks, Jinan 250353, China;4. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin 541004, China
Abstract:
Keywords:Image quality assessment  Realistic blur  Feature selection  Machine learning
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