Preferential Spectral Image Quality Model

  • D. Kalenova
  • P. Toivanen
  • V. Bochko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)


In this paper a novel method of spectral image quality characterization and prediction, preferential spectral image quality model is introduced. This study is based on the statistical image model that sets a relationship between the parameters of the spectral and color images, and the overall appearance of the image. It has been found that standard deviation of the spectra affects the colorfulness of the image, while kurtosis influences the highlight reproduction or, so called vividness. The model presented in this study is an extension of a previously published spectral color appearance model. The original model has been extended to account for the naturalness constraint, i.e. the degree of correspondence between the image reproduced and the observer’s perception of the reality. The study shows that the presented preferential spectral image quality model is efficient in the task of quality of spectral image evaluation and prediction.


Image Quality Spectral Image Preference Distribution Function Natural Scene Usefulness Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • D. Kalenova
    • 1
  • P. Toivanen
    • 1
  • V. Bochko
    • 1
  1. 1.Spectral Image Processing and Analysis Group, Laboratory of Information Processing, Department of Information TechnologyLappeenranta University of TechnologyLappeenrantaFinland

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