Pooling Spike Neural Network for Acceleration of Global Illumination Rendering

The generation of photo-realistic images is a major topic in computer graphics. By using the principles of physical light propagation, images that are indistinguishable from real photographs can be generated. However, this computation is a very time-consuming task. When simulating the real behavior of light, individual images can take hours to be of sufficient quality. This paper proposes a bio-inspired architecture with spiking neurons for acceleration of global illumination rendering. This architecture with functional parts of sparse encoding, learning and decoding consists of a robust convergence measure on blocks. Feature, concatenation and prediction pooling coupled with three pooling operators: convolution, average and standard deviation are used in order to separate noise from signal. The pooling spike neural network (PSNN) represents a non-linear mapping from stochastic noise features of rendering images to their quality visual scores. The system dynamic, that computes a learning parameter for each image based on its level of noise, is a consistent temporal framework where the precise timing of spikes is employed for information processing. The experiments are conducted on a global illumination set which contains diverse image distortions and large number of images with different noise levels. The result of this study is a system composed from only two spike pattern association neurons (SPANs) suitably adopted to the quality assessment task that accurately predict the quality of images with a high agreement with respect to human psycho-visual scores. The proposed spike neural network has also been compared with support vector machine (SVM). The obtained results show that the proposed method gives promising efficiency.