We categorize comprehensively image quality measures, extend measures defined for gray scale images to their multispectral case, and propose novel image quality measures. The statistical behavior of the measures and their sensitivity to various kinds of distortions, data hiding and coding artifacts are investigated via Analysis of Variance techniques. Their similarities or differences have been illustrated by plotting their Kohonen maps. Measures that give consistent scores across an image class and that are sensitive to distortions and coding artifacts are pointed out. We present techniques for steganalysis of images that have been potentially subjected to watermarking or steganographic algorithms. Our hypothesis is that watermarking and steganographic schemes leave statistical evidence that can be exploited for detection with the aid of image quality features and multivariate regression analysis. The steganalyzer is built using multivariate regression on the selected quality metrics. In the absence of the ground-truth, a common reference image is obtained based on blurring. Simulation results with the chosen feature set and well-known watermarking and steganographic techniques indicate that our approach is able to reasonably accurately distinguish between marked and unmarked images. We also present a technique that provides progressive transmission and near-lossless compression in one single framework. The proposed technique produces a bitstream that results in progressive reconstruction of the image just like what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides near-lossless reconstruction with respect to a given bound after each layer of the successively refinable bitstream is decoded. Experimental results for both lossless and near-lossless cases are presented, which are competitive with the state-of-the-art compression schemes.