Image Quality Evaluation in Security Imaging Systems

The subjective image quality of image or video information is a crucial item in security imaging systems. During the last five years our lab has tested and verified various approaches to the image compression for security purposes and the evaluation of subjective image quality. In the paper we discuss selected important facts related to the subjective image quality evaluation and we present some anomalous experimental behavior of image compression techniques. An object-defined approach is investigated and advantageous characteristics of chosen methods are deployed to achieve the optimal performance of the surveillance video coder. Among others, we propose to use the artificial neural network (ANN) to predict resulting image quality rating scores. The proposed quality assessment model has been trained and tested using a set of grayscale images distorted by selected image compression algorithms