Digital video communication has evolved into an important field in the past few years. There have been significant advances in compression and transmission techniques, which have made possible to deliver high quality video to the end user. In particular, the advent of new technologies has allowed the creation of many new telecommunication services (e.g., direct broadcast satellite, digital television, high definition TV, video teleconferencing, Internet video). To quantify the performance of a digital video communication system, it is important to have a measure of video quality changes at each of the communication system stages. Since in the majority of these applications the transformed or processed video is destined for human consumption, humans will ultimately decide if the operation was successful or not. Therefore, human perception should be taken into account when trying to establish the degree to which a video can be compressed, deciding if the video transmission was successful, or deciding whether visual enhancements have provided an actual benefit. Measuring the quality of a video implies a direct or indirect comparison of the test video with the original video. The most accurate way to determine the quality of a video is by measuring it using psychophysical experiments with human subjects (ITU-R, 1998). Unfortunately, psychophysical experiments are very expensive, time-consuming and hard to incorporate into a design process or an automatic quality of service control. Therefore, the ability to measure video quality accurately and efficiently, without using human observers, is highly desirable in practical applications. Good video quality metrics can be employed to monitor video quality, compare the performance of video processing systems and algorithms, and to optimize the algorithms and parameter settings for a video processing system. With this in mind, fast algorithms that give a physical measure (objective metric) of the video quality are used to obtain an estimate of the quality of a video when being transmitted, received or displayed. Customarily, quality measurements have been largely limited to a few objective measures, such as the mean absolute error (MAE), the mean square error (MSE), and the peak signal-to-noise ratio (PSNR), supplemented by limited subjective evaluation. Although the use of such metrics is fairly standard in published literature, it suffers from one major weakness. The outputs of these measures do not always correspond well with human judgements of quality. In the past few years, a big effort in the scientific community has been devoted to the development of better video quality metrics that correlate well with the human perception of quality (Daly, 1993; Lubin, 1993; Watson et al., 2001; Wolf et al., 1991). Although much Source: Digital Video, Book edited by: Floriano De Rango, ISBN 978-953-7619-70-1, pp. 500, February 2010, INTECH, Croatia, downloaded from SCIYO.COM