期刊
EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
卷 -, 期 -, 页码 -出版社
SPRINGER
DOI: 10.1186/1687-5281-2013-54
关键词
-
资金
- NWO [639.021.230]
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.
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