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Title: Circular-ELM for the reduced-reference assessment of perceived image quality
Authors: Decherchi, S.
Gastaldo, P.
Zunino, R.
Cambria, E. 
Redi, J.
Keywords: Circular backpropagation
Extreme learning machine
Image quality assessment
Issue Date: 15-Feb-2013
Citation: Decherchi, S., Gastaldo, P., Zunino, R., Cambria, E., Redi, J. (2013-02-15). Circular-ELM for the reduced-reference assessment of perceived image quality. Neurocomputing 102 : 78-89. ScholarBank@NUS Repository.
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. © 2012 Elsevier B.V.
Source Title: Neurocomputing
ISSN: 09252312
DOI: 10.1016/j.neucom.2011.12.050
Appears in Collections:Staff Publications

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