Perceptual similarity between color images using fuzzy metrics |
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| Affiliation: | 1. Department of Mathematics, University of Latvia, Latvia;2. Instituto Universitario de Matemática Pura y Aplicada, Universidad Politécnica de Valencia, Spain;1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;1. State Key Lab of CAD&CG, Zhejiang University, China;2. Software School of Xiamen University, China;1. Information and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan;2. Institute of Computer Science and Technology, Peking University, Beijing, China;3. Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan;1. Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur 721302, India;2. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India;1. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China;2. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| Abstract: | In many applications of the computer vision field measuring the similarity between (color) images is of paramount importance. However, the commonly used pixelwise similarity measures such as Mean Absolute Error, Peak Signal to Noise Ratio, Mean Squared Error or Normalized Color Difference do not match well with perceptual similarity. Recently, it has been proposed a method for gray-scale image similarity that correlates quite well with the perceptual similarity and it has been extended to color images. In this paper we use the basic ideas in this recent work to propose an alternative method based on fuzzy metrics for perceptual color image similarity. Experimental results employing a survey of observations show that the global performance of our proposal is competitive with best state of the art methods and that it shows some advantages in performance for images with low correlation among some image channels. |
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| Keywords: | Color imaging Fuzzy logic Fuzzy metrics Perceptual image similarity Color similarity Perceptual observations Low level image processing Color image quality |
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