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From literature it is known that full-reference visual quality indices are a poor fit for the estimation of visual security for selective encryption. The question remains whether no-reference visual quality indices can perform where full reference indices falter. Furthermore, no-reference visual quality indices frequently use machine learning to train a model of natural scene statistics. It would be of interest to be able to gauge the impact of learning statistics from selectively encrypted images on performance as quality estimators for encryption. In the following we will answer these two questions.

Applicability of No-Reference Visual Quality Indices for Visual Security Assessment