Abstract
A model that can predict an end user satisfaction or QoE (Quality of Experience) directly from the network QoS (Quality of Service) is still illusive in the field of image processing and is completely absent in multi-layered video. This motivates the derivation of a meaningful QoS to QoE mapping function to allow one to be predicted in the absence of the other. This paper presents an affine fuzzy logic based system that can map the QoS to QoE and can be extended to layered video streaming. The proposed methodology employs a learning system which optimizes the coded layered video for best QoE. Four QoS parameters arc chosen as the inputs of the designed model, while the output is the Peak Signal-to-Noise Ratio (PSNR). The designed membership functions and the fuzzy rules extracted from the input and the output enable the proposed model to identify and learn the video QoE.