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Quality assessment is a central issue in the design, implementation, and performance testing of all systems. Digital signal processing systems generally deal with visual information that are meant for human consumption. An image, a video, or a 3D model may go through different stages of processing before being presented to a human observer, and each stage of processing may introduce distortions that could reduce the quality of the final display. To conceive quantitative metrics that can automatically predict the perceived quality, the way humans perceive such distortions has to be taken into account and can be greatly beneficial for quality assessment. In general, an objective quality metric plays an important role in a broad range of applications, such as visual information acquisition, compression, analysis and watermarking. Quality metrics can be used to optimize algorithm parameter settings and to benchmark different processing systems and algorithms. In this dissertation, new objective quality metrics that take into account how distortions are perceived, are proposed and three different signal processing systems are considered: video watermarking, video object segmentation and 3D models watermarking. First, two new objective metrics for watermarked video quality assessment are proposed. Based on several different watermarking algorithms and video sequences, the most predominant distortions are identified as spatial noise and temporal flicker. Corresponding metrics are designed and their performance is tested through subjective experiments. Second, the problem of video object segmentation quality evaluation is discussed, proposing both subjective evaluation methodology and perceptual objective quality metric. Since a perceptual metric requires a good knowledge of the kinds of artifacts present in segmented video objects, the most typical artifacts are synthetically generated. Psychophysical experiments are carried out to study the perception of individual artifacts by themselves or combined. A new metric is proposed by combining the individual artifacts using the Minkowski metric and a linear model. An in-depth evaluation of the performance of the proposed method is carried out. The obtained perceptual metric is also used to benchmark different video object segmentation techniques for general frameworks as well as specific applications, ranging from object-based coding to video surveillance. Third, two novel metrics for watermarked 3D model quality assessment are proposed on the basis of two subjective experiments. The first psychophysical experiment is carried out to investigate the perception of distortions caused by watermarking 3D models. Two roughness estimation metrics have been devised to perceptually measure the amount of visual distortions introduced on the model's surface. The second psychophysical experiment is conducted in order to validate the two proposed metrics with other watermarking algorithms. All of the proposed metrics for the three kinds of visual information processing systems are based on the results of the psychophysical experiments. Subjective tests are carried out to study and characterize the impact of distortions on human perception. An evaluation of the performance of these perceptual metrics with respect to the most common state of the art objective metrics is performed. The comparison shows a better performance of the proposed perceptual metrics than that of the state of the art metrics. The performance is investigated in terms of correlation with subjective opinion. The results demonstrate that including the perception of distortions in objective metrics is a reliable approach and improve the performance of such metrics.