Implementation of perceptual measure Picture Quality Scale with neural network to evaluate distortions in compressed images

Abstract

The perceptual measures are often used to assess distortions in image compression. In this article different images were evaluated using the Picture Quality Scale (PQS) measure with neural network. On the basis of original and compressed images the local distortions in the compressed image are calculated. Then five factors {F1, F2, F3, F4, F5}, which represent these distortions are computed, and used to evaluate correlations among them by the covariance matrix. The new values are put to the input of neural network, to calculate the single PQS value. During the process of learning the neural network the best value PQS, which reflects the largest degree of particular distortions in the compressed images is obtained. The images are divided into three groups: faces, landscapes and shapes. The process of learning is controlled by the subjective measure Mean Opinion Score (MOS) with 15 observers.