|
Robustness and Prediction Accuracy of Machine Learning for Objective Visual Quality Assessment Host Publication: 22nd European Signal Processing Conference, EUSIPCO 2014 Authors: A. Hines, P. Kendrick, A. Barri, M. Narwaria and J. Redi Publisher: IEEE Publication Year: 2014 ISBN: 978-0-9928626-1-9
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, speci?cally 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. External Link.
|
