Objective metrics for visual quality assessment usually improve their reliability by explicitly modeling the highly non-linear behavior of human perception; as a result, they often are 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 (HVS). Several studies already proved the ability of ML-based approach to address visual quality assessment. Indeed, 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
MACHINE LEARNING SOLUTIONS FOR OBJECTIVE VISUAL QUALITY ASSESSMENT
GASTALDO, PAOLO;
2012-01-01
Abstract
Objective metrics for visual quality assessment usually improve their reliability by explicitly modeling the highly non-linear behavior of human perception; as a result, they often are 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 (HVS). Several studies already proved the ability of ML-based approach to address visual quality assessment. Indeed, 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
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