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Perceptual assessment of image quality in multimedia technology

Fast development of multimedia technology during the last two decades has brought different approach to the evaluation of image quality. In most of the cases, multimedia technology applications do not rely on the image fidelity criterion but the human impression plays the main role. A model for perceptual assessment of image quality in multimedia technology is presented in this paper. The model exploits properties of the human visual system (HVS) while utilizing steerable pyramidal decomposition. Image distortion features are based on Jeffrey divergence (JD) as a metric between probability distributions of original and distorted image signal values in each subband of steerable pyramid. Mean square error (MSE) is also computed. Data preprocessing using mutual information (MI) approach has been used to get a smaller set of objective distortion features describing the perceived image quality with reasonable precision. The impairment feature vector is processed by the radial basis function (RBF) artificial neural network (ANN) to allow simple adaptation of the model in respect to the required mode of operation, fidelity or impressiveness based. Parameters of the ANN are adjusted using mean opinion scores (MOS) obtained from the group of assessors. The presented system mimics an assessment process with human subjects. Model performance is verified comparing predicted quality and scores from human observers.