Supporting visual quality assessment with machine learning

Supporting visual quality assessment with machine learning

Gastaldo, P.
Zunino, R.
Redi, J.

Electrical Engineering, Mathematics and Computer Science

Intelligent Systems

2013-09-23

Objective metrics for visual quality assessment often base their reliability on the explicit modeling of the highly non-linear behavior of human perception; as a result, they may be complex and computationally expensive. Conversely, machine learning (ML) paradigms allow to tackle the quality assessment task from a different perspective, as the eventual goal is to mimic quality perception instead of designing an explicit model the human visual system. Several studies already proved the ability of ML-based approaches to address visual quality assessment; nevertheless, these paradigms are highly prone to overfitting, and their overall reliability may be questionable. In fact, a prerequisite for successfully using ML in modeling perceptual mechanisms is a profound understanding of the advantages and limitations that characterize learning machines. This paper illustrates and exemplifies the good practices to be followed.

http://resolver.tudelft.nl/uuid:8b448b12-ec18-4cc7-8f25-0aa95c51159a

https://doi.org/10.1186/1687-5281-2013-54

SpringerOPen

1687-5281

EURASIP Journal on Image and Video Processing, no. 54 (2013)

Institutional Repository

journal article

© 2013 Gastaldo et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.