Hines, A., Kendrick, P., Barri, A., Narwaria, M., & Redi, J. (2014). Robustness and Prediction Accuracy of Machine Learning for Objective Visual Quality Assessment. In 2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO) (pp. 2130-2134). (European Signal Processing Conference Proceedings). IEEE. http://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569923531.pdf

@inproceedings{8b255c853aae44a6bd7442f2fb7e0c0d,
title = "Robustness and Prediction Accuracy of Machine Learning for Objective Visual Quality Assessment",
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.",
keywords = "image quality assessment, SSIM, neural networks, machine learning",
author = "Andrew Hines and Paul Kendrick and Adriaan Barri and Manish Narwaria and Judith Redi",
year = "2014",
language = "English",
isbn = "978-0-9928626-1-9",
series = "European Signal Processing Conference Proceedings",
publisher = "IEEE",
pages = "2130--2134",
booktitle = "2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO)",
note = "22nd European Signal Processing Conference, EUSIPCO 2014 ; Conference date: 01-09-2014 Through 05-09-2014",
}