  With the increasing demand for image-based applications, the efficient and
reliable evaluation of image quality has increased in importance. Measuring the
image quality is of fundamental importance for numerous image processing
applications, where the goal of image quality assessment (IQA) methods is to
automatically evaluate the quality of images in agreement with human quality
judgments. Numerous IQA methods have been proposed over the past years to
fulfill this goal. In this paper, a survey of the quality assessment methods
for conventional image signals, as well as the newly emerged ones, which
includes the high dynamic range (HDR) and 3-D images, is presented. A
comprehensive explanation of the subjective and objective IQA and their
classification is provided. Six widely used subjective quality datasets, and
performance measures are reviewed. Emphasis is given to the full-reference
image quality assessment (FR-IQA) methods, and 9 often-used quality measures
(including mean squared error (MSE), structural similarity index (SSIM),
multi-scale structural similarity index (MS-SSIM), visual information fidelity
(VIF), most apparent distortion (MAD), feature similarity measure (FSIM),
feature similarity measure for color images (FSIMC), dynamic range independent
measure (DRIM), and tone-mapped images quality index (TMQI)) are carefully
described, and their performance and computation time on four subjective
quality datasets are evaluated. Furthermore, a brief introduction to 3-D IQA is
provided and the issues related to this area of research are reviewed.
