6533b870fe1ef96bd12cfd32

RESEARCH PRODUCT

Perceptual similarity between color images using fuzzy metrics

Svetlana GreovaSamuel Morillas

subject

Color histogramMean squared errorColor similarityComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologySimilarity measureFuzzy logicLow level image processingFuzzy metricsSimilarity (network science)0202 electrical engineering electronic engineering information engineeringMedia TechnologyComputer visionElectrical and Electronic EngineeringMathematicsPerceptual image similarityColor differencebusiness.industryColor image020206 networking & telecommunicationsPattern recognitionColor imagingPeak signal-to-noise ratioPerceptual observationsColor image qualityFuzzy logicComputer Science::Computer Vision and Pattern RecognitionSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusiness

description

A method to measure the similarity between color images is proposed.Correlation among the color image channels is taken into account.Proposed similarity measure is based on fuzzy metrics because of their advantages.The proposal matches well with the perceptual visual similarity between color images. 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.

yearjournalcountryeditionlanguage
2016-01-01Journal of Visual Communication and Image Representation
https://doi.org/10.1016/j.jvcir.2015.04.003