MACHINE LEARNING SOLUTIONS FOR OBJECTIVE VISUAL QUALITY ASSESSMENT

Description

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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