Circular-ELM for the reduced-reference assessment of perceived image quality

Authors:
Sergio Decherchi;Paolo Gastaldo;Rodolfo Zunino;Erik Cambria;Judith Redi
Affiliations:
Department of Drug Discovery and Development-Fondazione Istituto Italiano di Tecnologia (IIT), Morego, Genova, Italy;Department of Naval, Electric, Electronic and Telecommunications Engineering, DITEN, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy;Department of Naval, Electric, Electronic and Telecommunications Engineering, DITEN, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy;Temasek Laboratories, National University of Singapore, 5A Engineering Drive 1, Singapore 117411, Singapore;Department of Intelligent Systems, Delft University of Technology, Mekelweg 4, 2628CD Delft, The Netherlands
Venue:
Neurocomputing
Year:
2013

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Abstract

Providing a satisfactory visual experience is one of the main goals for present-day electronic multimedia devices. All the enabling technologies for storage, transmission, compression, rendering should preserve, and possibly enhance, the quality of the video signal; to do so, quality control mechanisms are required. These mechanisms rely on systems that can assess the visual quality of the incoming signal consistently with human perception. Computational Intelligence (CI) paradigms represent a suitable technology to tackle this challenging problem. The present research introduces an augmented version of the basic Extreme Learning Machine (ELM), the Circular-ELM (C-ELM), which proves effective in addressing the visual quality assessment problem. The C-ELM model derives from the original Circular BackPropagation (CBP) architecture, in which the input vector of a conventional MultiLayer Perceptron (MLP) is augmented by one additional dimension, the circular input; this paper shows that C-ELM can actually benefit from the enhancement provided by the circular input without losing any of the fruitful properties that characterize the basic ELM framework. In the proposed framework, C-ELM handles the actual mapping of visual signals into quality scores, successfully reproducing perceptual mechanisms. Its effectiveness is proved on recognized benchmarks and for four different types of distortions.