No-Reference Quality Assessment of JPEG Images by Using CBP Neural Networks

  • Paolo Gastaldo
  • Giovanni Parodi
  • Judith Redi
  • Rodolfo Zunino
Conference paper

DOI: 10.1007/978-3-540-74695-9_58

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4669)
Cite this paper as:
Gastaldo P., Parodi G., Redi J., Zunino R. (2007) No-Reference Quality Assessment of JPEG Images by Using CBP Neural Networks. In: de Sá J.M., Alexandre L.A., Duch W., Mandic D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg

Abstract

Imaging algorithms often require reliable methods to evaluate the quality effects of the visual artifacts that digital processing brings about. This paper adopts a no-reference objective method for predicting the perceived quality of images in a deterministic fashion. Principal Component Analysis is first used to assemble a set of objective features that best characterize the information in image data. Then a neural network, based on the Circular Back-Propagation (CBP) model, associates the selected features with the corresponding predictions of quality ratings and reproduces the scores process of human assessors. The neural model allows one to decouple the process of feature selection from the task of mapping features into a quality score. Results on a public database for an image-quality experiment involving JPEG compressed-images and comparisons with existing objective methods confirm the approach effectiveness.

Keywords

Image quality assessment feedforward neural networks JPEG 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Paolo Gastaldo
    • 1
  • Giovanni Parodi
    • 1
  • Judith Redi
    • 1
  • Rodolfo Zunino
    • 1
  1. 1.Dept. of Biophysical and Electronic Engineering (DIBE), University of Genoa, Via Opera Pia 11a, 16145, GenoaItaly

Personalised recommendations