A large amount of digital visual data is being distributed and communicated globally and the question of video quality control becomes a central concern. Unlike many signal processing applications, the intended receiver of video signals is nearly always the human eye. Video quality assessment algorithms must attempt to assess perceptual degradations in videos. My dissertation focuses on full reference methods of image and video quality assessment, where the availability of a perfect or pristine reference image/video is assumed. A large body of research on image quality assessment has focused on models of the human visual system. The premise behind such metrics is to process visual data by simulating the visual pathway of the eye-brain system. Recent approaches to image quality assessment, the structural similarity index and information theoretic models, avoid explicit modeling of visual mechanisms and use statistical properties derived from the images to formulate measurements of image quality. I show that the structure measurement in structural similarity is equivalent to contrast masking models that form a critical component of many vision based methods. I also show the equivalence of the structural and the information theoretic metrics under certain assumptions on the statistical distribution of the reference and distorted images. Videos contain many artifacts that are specific to motion and are largely temporal. Motion information plays a key role in visual perception of video signals. I develop a general, spatio-spectrally localized multi-scale framework for evaluating dynamic video fidelity that integrates both spatial and temporal aspects of distortion assessment. Video quality is evaluated in space and time by evaluating motion quality along computed motion trajectories. Using this framework, I develop a full-reference video quality assessment algorithm known as the MOtion-based Video Integrity Evaluation index, or MOVIE index. Lastly, and significantly, I conducted a large-scale subjective study on a database of videos distorted by present generation video processing and communication technology. The database contains 150 distorted videos obtained from 10 naturalistic reference videos and each video was evaluated by 38 human subjects in the study. I study the performance of leading, publicly available objective video quality assessment algorithms on this database. / text

This dissertation presents experimental studies on Brownian motion at fast time scales, as well as our recent developments in Thermal Noise Imaging which uses thermal motions of microscopic particles for spatial imaging. As thermal motions become increasingly important in the studies of soft condensed matters, the study of Brownian motion is not only of fundamental scientific interest but also has practical applications. Optical tweezers with a fast position-sensitive detector provide high spatial and temporal resolution to study Brownian motion at fast time scales. A novel high bandwidth detector was developed with a temporal resolution of 30 ns and a spatial resolution of 1 °A. With this high bandwidth detector, Brownian motion of a single particle confined in an optical trap was observed at the time scale of the ballistic regime. The hydrodynamic memory effect was fully studied with polystyrene particles of different sizes. We found that the mean square displacements of different sized polystyrene particles collapse into one master curve which is determined by the characteristic time scale of the fluid inertia effect. The particle’s inertia effect was shown for particles of the same size but different densities. For the first time the velocity autocorrelation function for a single particle was shown. We found excellent agreement between our experiments and the hydrodynamic theories that take into account the fluid inertia effect. Brownian motion of a colloidal particle can be used to probe three-dimensional nano structures. This so-called thermal noise imaging (TNI) has been very successful in imaging polymer networks with a resolution of 10 nm. However, TNI is not efficient at micrometer scale scanning since a great portion of image acquisition time is wasted on large vacant volume within polymer networks. Therefore, we invented a method to improve the efficiency of large scale scanning by combining traditional point-to-point scanning to explore large vacant space with thermal noise imaging at the proximity of the object. This method increased the efficiency of thermal noise imaging by more than 40 times. This development should promote wider applications of thermal noise imaging in the studies of soft materials and biological systems. / text

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The amount of digital image and video content being generated and shared has grown explosively in the recent past. The primary goal of image and video communication systems is to achieve the best possible visual quality at a given rate constraint and channel conditions. In this dissertation, the focus is limited to image communication systems. In order to optimize the components of the communication system to maximize perceptual quality, it is important to use a good measure of quality. Even though this fact has been long recognized, the mean squared error (MSE), which is not the best measure of perceptual quality, has been a popular choice in the design of various components of an image communication system. Recent developments in the field of image quality assessment (IQA) have resulted in the development of powerful new algorithms. A few of these new algorithms include the structural similarity (SSIM) index, the visual information fidelity (VIF) criterion, and the visual signal to noise ratio (VSNR). The SSIM index is considered in this dissertation. I demonstrate that optimizing image processing algorithms for the SSIM index does indeed result in an improvement in the perceptual quality of the processed images. All the comparisons in this thesis are made against appropriate MSE-optimal equivalents. First, an SSIM-optimal linear estimator is derived and applied to the problem of image denoising. An algorithm for SSIM-optimal linear equalization is developed and applied to the problem of image restoration. Followed by the development of the linear solution, I addressed the problem of SSIM-optimal soft thresholding which is a nonlinear technique. The estimation, equalization, and soft-thresholding results all show a gain in the visual quality compared to their MSE-optimal counterparts. These solutions are typically used at the receiver of an image communication system. On the transmitter side of the system, bounds on the SSIM index as a function of the rate allocated to a uniform quantizer are derived.

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M. Tech. Electronic Engineering. / The Council for Scientific and Industrial Research (CSIR) in South Africa has developed a portable Multi-Spectral Imaging Camera (MultiCam) capable of capturing images in the Visual (VI), Infra Red (IR) and Ultra Violet (UV) bands. The images obtained from the MultiCam are used to perform diagnostics on high voltage transmission networks. To provide the user with a better understanding of the scene it is necessary to fuse the images. Due to the physical placement of the detectors and the separate zoom-control of each optical channel, a spatial misalignment or geometric distortion is introduced to the images. The images must be optimally aligned and registered before any fusion can be applied. The aim of this work is to investigate methods for the automatic registration and fusion of IR and VI images acquired by the MultiCam and to develop a set of algorithms for implementation on an embedded platform that could integrate with the existing MultiCam

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

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