Browsing by Subject "Natural scene statistics"
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Item Blind image and video quality assessment using natural scene and motion models(2013-05) Saad, Michele Antoine; Bovik, Alan C. (Alan Conrad), 1958-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.Item A closed-form correlation model of oriented bandpass natural images beyond adjacent responses(2015-05) Sinno, Zeina; Bovik, Alan C. (Alan Conrad), 1958-; Ghosh, JoydeepBuilding natural scene statistical models is crucial for a large set of applications starting from the design of faithful image and video quality metrics to image enhancing techniques. Most predominant statistical models of natural images characterize univariate distributions of divisively normalized bandpass responses or wavelet-like decomposition of them. Previous models focusing on these bandpass natural responses offer optimized solutions to numerous problems in image processing however, these models have not focused on finding a closed-form quantative model capturing the bivariate natural statistics. Towards the efforts for filling this gap, Su et. al recently modeled spatially horizontally neighboring bandpass image responses on multiple scales; however, the latter work did not cover the response of distant bandpass image responses with various spatial orientations. This work builds on Su. et al 's model and extends the closed-form correlation model to cover distant bandpass image responses, up to a distance of ten pixels; with multiple spatial orientations, encompassing all the discrete spatial angles for the lastly-mentioned distances on multiple scales.Item Natural scene statistics based blind image quality assessment in spatial domain(2011-05) Mittal, Anish; Bovik, Alan C. (Alan Conrad), 1958-; Cormack, Lawrence K.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).Item Pattern detection in natural images(2016-12) Sebastian, Stephen P.; Geisler, Wilson S.; Bovik, Alan; Hayhoe, Mary; Cormack, Lawrence K; Seideman, EyalA fundamental visual task is to detect target objects within a background scene. Using relatively simple stimuli, vision science has identified several major factors that affect detection thresholds, such as the luminance of the background, the contrast of the background, the spatial similarity of the background to the target, and uncertainty due to random variations in the properties of the background and in the amplitude of the target. Here I use a new experimental approach together with a theoretical analysis based on signal detection theory, to discover how these factors affect detection in natural scenes. First, I sorted a large collection of natural image backgrounds into multidimensional bins, where each bin corresponds to a narrow range of luminance, contrast and similarity. Detection thresholds were measured by randomly sampling a natural image background from a bin on each trial. In low uncertainty conditions both the bin and the amplitude of the target were blocked and in high uncertainty conditions the bin and amplitude varied randomly on each trial. I found that thresholds increased approximately linearly along all three dimensions and that detection accuracy was unaffected by bin and amplitude uncertainty. The entire set of results was predicted from first principles by a normalized matched template detector, where the dynamic normalizing factor follows directly from the statistical properties of the natural backgrounds. This model assumed that the properties of the background underneath the target were constant across the image, but in natural images this is often not the case. Therefore, in a separate experiment, I measured detection thresholds on backgrounds where the contrast was modulated underneath the target. I found that varying the contrast underneath the target signal had a substantial effect on detectability, and that the pattern of results was predicted by an ideal observer that weighted its response based on an estimate of the local contrast (under the target). This suggests that the human visual system is able to use the varying properties of the background under the target in an near optimal way. Taken together, the results provide a new explanation for some classic laws of psychophysics and their underlying neural mechanisms.Item Scene statistics in 3D natural environments(2010-08) Liu, Yang, 1976-; Bovik, Alan C. (Alan Conrad), 1958-; Cormack, Lawrence K.; Geisler, Wilson G.; Vishwanath, Sriram; Ghosh, JoydeepIn 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.Item Utilizing natural scene statistics and blind image quality analysis of infrared imagery(2013-08) Kaser, Jennifer Yvonne; Bovik, Alan C. (Alan Conrad), 1958-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.