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In digital transmission, images may undergo quality degradation due to lossy compression and error-prone channels. Efficient measurement tools are needed to quantify induced distortions and to predict their impact on perceived quality. In this paper, an artificial neural network (ANN) is proposed for perceptual image quality assessment. The quality prediction is based on structural image features such as blocking, blur, image activity, and intensity masking. Training and testing of the ANN is performed with reference to subjective experiments and the obtained mean opinion scores (MOS). It is shown that the proposed ANN is capable of predicting MOS over a wide range of image distortions. This applies to both cases, when reference information about the structure of the original image is available to the ANN but also in absence of this knowledge. The considered ANN would therefore be well suited for combination with link adaption techniques.