Ambiguity of objective image quality metrics: A new methodology for performance evaluation

Autor: Lukas Krasula, Junghyuk Lee, Toinon Vigier, Manri Cheon, Jong-Seok Lee, Patrick Le Callet
Přispěvatelé: Image Perception Interaction (IPI), Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
Computer science
Image quality
media_common.quotation_subject
02 engineering and technology
Interval (mathematics)
Machine learning
computer.software_genre
0202 electrical engineering
electronic engineering
information engineering
FOS: Electrical engineering
electronic engineering
information engineering
Performance measurement
Quality (business)
[INFO]Computer Science [cs]
Electrical and Electronic Engineering
ComputingMilieux_MISCELLANEOUS
media_common
business.industry
Image and Video Processing (eess.IV)
[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]
020206 networking & telecommunications
Ambiguity
Electrical Engineering and Systems Science - Image and Video Processing
Multimedia (cs.MM)
Signal Processing
Quality Score
Human visual system model
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
Metric (unit)
business
computer
Computer Science - Multimedia
Software
Zdroj: Signal Processing: Image Communication
Signal Processing: Image Communication, Elsevier, 2021, 93, pp.116150. ⟨10.1016/j.image.2021.116150⟩
ISSN: 0923-5965
1879-2677
Popis: Objective image quality metrics try to estimate the perceptual quality of the given image by considering the characteristics of the human visual system. However, it is possible that the metrics produce different quality scores even for two images that are perceptually indistinguishable by human viewers, which have not been considered in the existing studies related to objective quality assessment. In this paper, we address the issue of ambiguity of objective image quality assessment. We propose an approach to obtain an ambiguity interval of an objective metric, within which the quality score difference is not perceptually significant. In particular, we use the visual difference predictor, which can consider viewing conditions that are important for visual quality perception. In order to demonstrate the usefulness of the proposed approach, we conduct experiments with 33 state-of-the-art image quality metrics in the viewpoint of their accuracy and ambiguity for three image quality databases. The results show that the ambiguity intervals can be applied as an additional figure of merit when conventional performance measurement does not determine superiority between the metrics. The effect of the viewing distance on the ambiguity interval is also shown.
Databáze: OpenAIRE
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