Circular back-propagation network networks for measuring displayed image quality

P. Gastaldo, R. Zunino, I.E.J. Heynderickx, E. Vicario

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)


A system based on a neural-network estimates the perceived quality of digital pictures that had previously undergone image-enhancement algorithms. The objective system exploits the ability of feed-forward networks to handle multidimensional data with non-linear relationships. A Circular Back-Propagation network maps feature vectors into the associated quality ratings, thus estimating perceived quality. Feature vectors characterize the image at a global level by exploiting statistical properties of objective features, which are extracted on a block-by-block basis. A feature-selection procedure based on statistical analysis drives the composition of the objective metric set. Experimental results confirm the approach effectiveness, as the system provides a satisfactory approximation of subjective tests involving human voters.
Original languageEnglish
Title of host publication ICANN : international conference on artificial neural networks : proceedings, 2002, Madrid, Spain, August 28-30
Place of PublicationBerlin
Number of pages6
ISBN (Electronic)978-3-540-46084-8
ISBN (Print)978-3-540-44074-1
Publication statusPublished - 2002
Externally publishedYes

Publication series

NameLecture notes in computer science


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