Hybrid Neural Systems for Reduced-Reference Image Quality Assessment

  • Judith Redi
  • Paolo Gastaldo
  • Rodolfo Zunino
Conference paper

DOI: 10.1007/978-3-642-04277-5_69

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5769)
Cite this paper as:
Redi J., Gastaldo P., Zunino R. (2009) Hybrid Neural Systems for Reduced-Reference Image Quality Assessment. In: Alippi C., Polycarpou M., Panayiotou C., Ellinas G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg

Abstract

Reduced-reference paradigms are suitable for supporting real-time modeling of perceived quality, since they make use of salient features both from the target image and its original, undistorted version, without requiring the full original information. In this paper a reduced-reference system is proposed, based on a feature-based description of images which encodes relevant information on the changes in luminance distribution brought about by distortions. Such a numerical description feeds a double-layer hybrid neural system: first, the kind of distortion affecting the image is identified by a classifier relying on Support Vector Machines (SVMs); in a second step, the actual quality level of the distorted image is assessed by a dedicated predictor based on Circular Back Propagation (CBP) neural networks, specifically trained to assess image quality for a given artifact. The general validity of the approach is supported by experimental validations based on subjective quality data.

Keywords

Image quality assessment neural networks svm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Judith Redi
    • 1
  • Paolo Gastaldo
    • 1
  • Rodolfo Zunino
    • 1
  1. 1.Dept. of Biophysical and Electronic Engineering (DIBE)Genoa UniversityGenoaItaly

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