Abstract
Monitoring and controlling the user's perceived quality, in modern video services is a challenging proposition, mainly due to the limitations of current Image Quality Assessment (IQA) algorithms. Subjective Quality of Experience (QoE) is widely used to get a right impression, but unfortunately this can not be used in real world scenarios. In general, objective QoE algorithms represent a good substitution for the subjective ones, and they are split in three main directions: Full Reference (FR), Reduced Reference (RR), and No Reference (NR). From these three, the RR IQA approach offers a practical solution to assess the quality of an impaired image due to the fact that just a small amount of information is needed from the original image. At the same time, keeping in mind that we need automated QoE algorithms which are context independent, in this paper we introduce a novel stochastic RR IQA metric to assess the quality of an image based on Deep Learning, namely Restricted Boltzmann Machine Similarity Measure (RBMSim). RBMSim was evaluated on two benchmarked image databases with subjective studies, against objective IQA algorithms. The results show that its performance is comparable, or even better in some cases, with widely known FR IQA methods.