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author:

Zhang, K. (Zhang, K..) [1] | Zhao, T. (Zhao, T..) [2] | Chen, W. (Chen, W..) [3] | Niu, Y. (Niu, Y..) [4] | Hu, J. (Hu, J..) [5] | Lin, W. (Lin, W..) [6]

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Scopus

Abstract:

Super-Resolution (SR) algorithms aim to enhance the resolutions of images. Massive deep-learning-based SR techniques have emerged in recent years. In such case, a visually appealing output may contain additional details compared with its reference image. Accordingly, fully referenced Image Quality Assessment (IQA) cannot work well; however, reference information remains essential for evaluating the qualities of SR images. This poses a challenge to SR-IQA: How to balance the referenced and no-reference scores for user perception? In this paper, we propose a Perception-driven Similarity-Clarity Tradeoff (PSCT) model for SR-IQA. Specifically, we investigate this problem from both referenced and no-reference perspectives, and design two deep-learning-based modules to obtain referenced and no-reference scores. We present a theoretical analysis based on Human Visual System (HVS) properties on their tradeoff and also calculate adaptive weights for them. Experimental results indicate that our PSCT model is superior to the state-of-the-arts on SR-IQA. In addition, the proposed PSCT model is also capable of evaluating quality scores in other image enhancement scenarios, such as deraining, dehazing and underwater image enhancement. The source code is available at https://github.com/kekezhang112/PSCT.  © 1991-2012 IEEE.

Keyword:

Image quality assessment image super-resolution perception-driven similarity-clarity tradeoff

Community:

  • [ 1 ] [Zhang K.]Fuzhou University, Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou, 350108, China
  • [ 2 ] [Zhao T.]Fuzhou University, Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou, 350108, China
  • [ 3 ] [Zhao T.]Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350108, China
  • [ 4 ] [Chen W.]Fuzhou University, Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou, 350108, China
  • [ 5 ] [Chen W.]Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350108, China
  • [ 6 ] [Niu Y.]Fuzhou University, Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou, 350108, China
  • [ 7 ] [Niu Y.]Ministry of Education, Key Laboratory of Spatial Data Mining and Information Sharing, Fuzhou, 350108, China
  • [ 8 ] [Hu J.]Fuzhou University, Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou, 350108, China
  • [ 9 ] [Lin W.]Nanyang Technological University, School of Computer Science and Engineering, Jurong West, 639798, Singapore

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Source :

IEEE Transactions on Circuits and Systems for Video Technology

ISSN: 1051-8215

Year: 2024

Issue: 7

Volume: 34

Page: 5897-5907

8 . 3 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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