Robustness and prediction accuracy of machine learning for objective visual quality assessment
Citation:
A Hines, P Kendrick, A Barri, M Narwaria, JA Redi, Robustness and prediction accuracy of machine learning for objective visual quality assessment, EUSIPCO, Lisbon, Portugal, 2014Download Item:
Abstract:
Machine Learning (ML) is a powerful tool to support the
development of objective visual quality assessment metrics,
serving as a substitute model for the perceptual mechanisms
acting in visual quality appreciation. Nevertheless, the reli-
ability of ML-based techniques within objective quality as-
sessment metrics is often questioned. In this study, the ro-
bustness of ML in supporting objective quality assessment
is investigated, specifically when the feature set adopted for
prediction is suboptimal. A Principal Component Regres-
sion based algorithm and a Feed Forward Neural Network
are compared when pooling the Structural Similarity Index
(SSIM) features perturbed with noise. The neural network
adapts better with noise and intrinsically favours features ac-
cording to their salient content.
Author's Homepage:
http://people.tcd.ie/ahinesDescription:
PUBLISHEDLisbon, Portugal
Author: HINES, ANDREW
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EUSIPCOType of material:
Conference PaperAvailability:
Full text availableKeywords:
machine learning, neural networks, SSIM, image quality assessmentSubject (TCD):
Intelligent Content & Communications , Signal processingMetadata
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