## 2 Overview of Similarity-Based Full-Reference Image Quality Assessment Methods  
General-purpose image quality assessment methods may be divided into two major groups, namely subjective and objective methods. The main differences between these two generic approaches are related to the assumed application and required assessment time. The usefulness of the subjective methods in many practical applications is limited by the necessary long time spent on gathering the opinions of individual observers. Hence, in practical applications, objective metrics that may be automatically calculated are more desired. Nevertheless, during the development of objective methods, subjective metrics may be used for verification of their correspondence to human perception of various kinds of image distortions, hence they may be considered mainly in view of aesthetic purposes. The development of subjective methods is a troublesome and time-consuming task as it requires the engagement of many human observers who fill the questionnaires assessing the quality of the presented images containing various kinds and levels of image distortions. After the statistical analysis of their responses, together with outlier rejection and normalization of scores, it is possible to develop a database of images containing a number of images together with appropriate subjective quality evaluations expressed as Mean Opinion Scores (MOS) or Differential MOS values. During the last years, several such databases have been developed for natural images, as well as for stereoscopic images, video sequences, or even multiply distorted images, which are useful for the verification and performance evaluation of the objective image quality metrics. A comprehensive overview of many IQA databases, as well as various types of quality metrics, may be found, i.e., in the recent survey paper [27]. As the practical applicability of subjective methods is strongly limited by the time necessary to acquire the scores, for many tasks, including optimization of image filtering and lossy compression algorithms, the only possibility is the use of objective metrics which may be calculated automatically without the involvement of the human observers. However, such metrics may be further divided into three families: no-reference metrics (NR IQA), also referred to as \"blind\" metrics, which do not require the knowledge of the original image, reduced-reference metrics using partial reference information and the most popular full-reference metrics (FR IQA) based on the comparison of the distorted images with the \"pristine\" reference images without any distortions. Such perfect quality reference imagesare also included in the IQA databases, helpful mainly in the development of some new FR metrics. Despite the potentially wide areas of applications of \"blind\" metrics, their universality is still much lower in comparison to FR IQA solutions, causing a higher popularity of the latter approaches. Most of the general-purpose FR IQA methods are based on the assumption that both compared images represent the same scene but one of the images is corrupted by one or more types of distortions, such as, e.g., the presence of noise, blur, lossy compression, transmission errors, etc. Hence, during the comparison of images, any image registering or spatial adjustment operations, including shifting or rotations are not necessary. Assuming the availability of the reference image, many FR metrics have been proposed during recent years, which utilize the similarity calculation between the original and distorted images. A milestone in the development of such metrics has been the idea of Universal Image Quality Index (UIQI) [28], being the first region-based metric without the disadvantages of the conventional pixel-based metrics, such as Mean Squared Error (MSE) or Peak Signal-to-Noise Ratio (PSNR), known as poorly correlated with subjective quality scores.