Performance of Levenberg-Marquardt Backpropagation for Full Reference Hybrid Image Quality Metrics

Image quality analysis is to study the quality of images and develop methods to efficiently and swiftly determine the quality of images. It is an important process especially in this digital age whereby transmission, compression and conversion are compulsory. Therefore, this paper proposed a hybrid method to determine the image quality by using Levenberg-Marquardt Back-Propagation Neural Network (LMBNN). Three known quality metrics were combined as the input element to the network. A proper set of network properties was chosen to represent this element and was trained using Levenberg-Marquardt algorithm (trainlm) in MATLAB. From the preliminary simulation, a promising output result was obtained indicated by good performance metrics results and good regression fitting.