Image quality assessment has diverse applications. A number of image quality measures (IQMs) are proposed, but none is proved to be true representative of human perception of image quality. We have subjectively investigated spectral distance-based and human visual system (HVS)-based IQMs for their effectiveness in representing the human perception for images corrupted with white noise and Gaussian blur. Two sets of 160 images with various degrees of white noise and Gaussian blur are subjectively evaluated by 50 human subjects, resulting in 16 000 human judgments. On the basis of evaluations, image-independent human perception values are calculated. The perception values are plotted against spectral distance-based and HVS-based IQMs. The performance of quality measures is determined by graphical observations and polynomial curve fitting, resulting in best performance by HVS absolute norm and block spectral phase-magnitude error for white noise and Gaussian blur distortions, respectively. It is also observed that the performances of various quality measures differ for different noise distortions, suggesting the use of different quality measures for different noise types rather than a single universal IQM.