Image quality analysis

Image quality analysis is becoming more an \nd more important in this digital age. The \nmain objective of image quality analysis is to study the quality of image \ns \nand \ndevelop methods to efficiently and swiftly determine the quality of images. \nThis \nproject aims to study subjective and obj \ne \nctive assessm \nent methods of image quality \nas well as to draw correlations between each objective assessment and the subjective \nassessment. \nThree objective assessment methods were used in this project, the \nQuality Index algorithm developed by Zhou Wang \nand Alan C. B \novik \n, the \nPeak \nSignal \n- \nto \n- \nNoise Ratio ( \nPSNR \n) \nBl \nock \n- \nSet, and the \nMean Squared Error ( \nMSE \n) \ncalculating algorithm. \nBy doing so, a better understanding of what is actually \nrequired to develop an efficient image quality assessment method was gained. The \nresulting da \nta also indicated what type of objective assessment was most suitable for \nwhich type of impairment imposed upon an image. Finally, the conclusions of this \nstudy were used to develop a prototype of a neural network based image quality \nassessment method that \ncould be further enhanced as part of a further study to \neve \nntually develop an objective image quality analysis method that has a higher \ncorrelation to the subjective assessment method compared to the other objective \nassessment methods employed throughout \nthis project.