Robustness and Prediction Accuracy of Machine Learning for Objective Visual Quality Assessment

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.

ISBN:978-0-9928626-1-9

Publication year:2014

Keywords:image quality assessment, SSIM, neural networks, machine learning