In this dissertation, we conducted a stereoscopic eye tracking experiment using naturalistic stereo images. We analyzed low level 2D and 3D scene features at binocular fixations and randomly selected places. The results reveal that humans tend to fixate on regions with higher luminance variations, but lower disparity variations. Because of the often observed co-occurrence of luminance and depth changes in natural environments, the dichotomy between luminance features and disparity features inspired us to study the accurate statistics of 2D and 3D scene properties. Using a range map database, we studied the distribution of disparity in natural scenes. The natural disparity distribution has a high peak at zero, and heavier tails that are similar to a Laplace distribution. The relevance of natural disparity distribution to other studies in neurobiology and visual psychophysics are discussed in detail. We also studied luminance, range and disparity statistics in natural scenes using a co-registered luminance-range database. The distributions of bandpass 2D and 3D scene features can be well modeled by generalized Gaussian models. There are positive correlations between bandpass luminance and depth, which can be captured by varying shape parameters in the probability density functions of the generalized Gaussians. In another study on suprathreshold luminance and depth discontinuities, we show that observing a significant luminance edge at a significant depth edge is much more likely than at homogeneous depth surfaces. It is also true that a significant depth edge happens at a significant luminance edge with a greater probability than at homogeneous luminance regions. Again, the dependency between luminance and depth discontinuities can be modeled successfully by generalized Gaussians. We applied our statistical models in 3D natural scenes to stereo correspondence. A Bayesian framework is proposed to incorporate the bandpass disparity prior, and the luminance-disparity dependency in the likelihood function. We compared our algorithm with a classical simulated annealing method based on heuristically defined energy functions. The computed disparity maps show great improvements both perceptually and objectively. / text

We tackle the problems of no-reference/blind image and video quality evaluation. The approach we take is that of modeling the statistical characteristics of natural images and videos, and utilizing deviations from those natural statistics as indicators of perceived quality. We propose a probabilistic model of natural scenes and a probabilistic model of natural videos to drive our image and video quality assessment (I/VQA) algorithms respectively. The VQA problem is considerably different from the IQA problem since it imposes a number of challenges on top of the challenges faced in the IQA problem; namely the challenges arising from the temporal dimension in video that plays an important role in influencing human perception of quality. We compare our IQA approach to the state of the art in blind, reduced reference and full-reference methods, and we show that it is top performing. We compare our VQA approach to the state of the art in reduced and full-reference methods (no blind VQA methods that perform reliably well exist), and show that our algorithm performs as well as the top performing full and reduced reference algorithms in predicting human judgments of quality. / text

With the increasing number and affordability of image capture devices, there is an increasing demand to objectively analyze and compare the quality of images. Image quality can also be used as an indicator to determine if the source image is of high enough quality to perform analysis on. When applied to real world scenarios, use of a blind algorithm is essential since a flawless reference image typically is unavailable. Recent research has shown promising results in no reference image quality utilizing natural scene statistics in the visual image space. Research has also shown that although the statistical profiles vary slightly, there are statistical regularities in IR images as well which would indicate that natural scene statistical models may be able to be applied. In this project, I will analyze BRISQUE quality features of IR images and determine if the algorithm can successfully be applied to IR images. Additionally, in order to validate the usefulness of these techniques, the BRISQUE quality features are analyzed using a detection algorithm to determine if they can be used to predict conditions which may cause missed detections. / text

Natural scene statistics (NSS) have played an increasingly important role in both our understanding of the function and evolution of the human vision system, and in the development of modern image processing applications. Because depth/range, i.e., egocentric distance, is arguably the most important thing a visual system must compute (from an evolutionary perspective), the joint statistics between natural image and depth/range information are of particular interest. However, while there exist regular and reliable statistical models of two-dimensional (2D) natural images, there has been little work done on statistical modeling of natural luminance/chrominance and depth/disparity, and of their mutual relationships. One major reason is the dearth of high-quality three-dimensional (3D) image and depth/range database. To facilitate research progress on 3D natural scene statistics, this dissertation first presents a high-quality database of color images and accurately co-registered depth/range maps using an advanced laser range scanner mounted with a high-end digital single-lens reflex camera. By utilizing this high-resolution, high-quality database, this dissertation performs reliable and robust statistical modeling of natural image and depth/disparity information, including new bivariate and spatial oriented correlation models. In particular, these new statistical models capture higher-order dependencies embedded in spatially adjacent bandpass responses projected from natural environments, which have not yet been well understood or explored in literature. To demonstrate the efficacy and effectiveness of the advanced NSS models, this dissertation addresses two challenging, yet very important problems, depth estimation from monocular images and no-reference stereoscopic/3D (S3D) image quality assessment. A Bayesian depth estimation framework is proposed to consider the canonical depth/range patterns in natural scenes, and it forms priors and likelihoods using both univariate and bivariate NSS features. The no-reference S3D image quality index proposed in this dissertation exploits new bivariate and correlation NSS features to quantify different types of stereoscopic distortions. Experimental results show that the proposed framework and index achieve superior performance to state-of-the-art algorithms in both disciplines. / text

We propose a natural scene statistic based quality assessment model Refer- enceless Image Spatial QUality Evaluator (RISQUE) which extracts marginal statistics of local normalized luminance signals and measures 'un-naturalness' of the distorted image based on measured deviation of them. We also model distribution of pairwise products of adjacent normalized luminance signals providing us with orientation distortion information. Although multi-scale, the model is defined in the space domain avoiding costly frequency or wavelet transforms. The frame work is simple, fast, human perception based and shown to perform statistically better than other proposed no reference algorithms and full reference structural similarity index(SSIM). / text