Perception of noise in global illumination algorithms based on spiking neural network

This paper proposes a reduced reference quality assessment model based on spiking neural network (SNN) in order to predict which image highlights perceptual noise in unbiased global illumination algorithms. These algorithms provide photo-realistic images by increasing the number of paths as proved by Monte Carlo theory. The objective is to find the number of paths that are required in order to ensure that most of the observers cannot perceive noise in any part of the image. A comparative study of this model with human psycho-visual scores demonstrates the good consistency between these scores and the learning model quality measures. The proposed model that uses a simple architecture composed only from two parallel spike pattern association neurons (SPANs) has been also compared with other learning model like SVM and gives satisfactory performance.