TOPQS Color Local Visual Distortion Maps

  • Maria Skublewska-Paszkowska
  • Jakub Smołka
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 102)


Image quality is an important aspect which should be taken into consideration during image processing. Quality can be measured using various methods, one of which is a perceptual Picture Quality Scale measure. It allows to verify whether the distortions in the image are visible to human beings and to what extent. Unfortunately this measure needs assessments from a group of observers. On their basis a single value is computed that corresponds to the amount of visual distortions present in image. Combining this measure together with a neural network allows to eliminate the need for human observers. This simplifies the assessment of image deformations and permits implementation that can be used for visualization of the distortions. For the purpose of the following paper the PQS measure was implemented with neural network resulting in a new measure called TOPQS. It was in turn used to obtain color local image visual distortions. They were computed using two images: the original and the processed one. Corresponding parts of these images were compared and the TOPQS value, which evaluates local distortions, was obtained. Once the images were analyzed local distortion maps were generated.


Neural Network Mean Opinion Score Local Distortion Contrast Sensitivity Function Visual Distortion 
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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  1. 1.
    Cyganek, B.: Three-dimmentional computer processing. EXIT (2002)Google Scholar
  2. 2.
    Kotani, K., Gan, Q., Miyahara, M., Algazi, V.: Objective Picture Quality Scale for Color Image Coding. IEEE Image Processing 3 (October 1995)Google Scholar
  3. 3.
    Miyahara, M., Kotani, K., Ralph Algazi, V.: Objective Picture Quality Scale (PQS) for Image Coding. IEEE Transactions on Communications 46(9) (September 1998)Google Scholar
  4. 4.
    Tadeusiewicz, R., Gąciarz, T., Borowik, B., Leper, B.: Reveal the neural network features using C# programs, Kraków (2007)Google Scholar
  5. 5.
    Wu, H.R., Rao, K.R.: Digital Video Image Quality and Perceptual Coding. CRC Press Taylor & Francis Group, London, New York (2006)Google Scholar
  6. 6.
    Zhang, X., Wandell Brian, A.: A Spatial Extension Of CIELAB For Digital Color Image Reproduction. Department of Psychology, Stanford University StanfordGoogle Scholar
  7. 7.
    Skublewska-Paszkowska, M.: The usage of perceptual measure Picture Quality Scale measure with neural network to evaluate local distortions in wavelet compressed images. Polish Journal of Environmental Studies Hard Olsztyn 17(3B) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maria Skublewska-Paszkowska
    • 1
  • Jakub Smołka
    • 1
  1. 1.Institute of Computer ScienceLublin University of TechnologyPoland

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