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Progress in multimedia technologies has resulted in a plethora of services and devices that capture, compress, transmit and display audiovisual stimuli. Humans -- the ultimate receivers of such stimuli -- now have access to visual entertainment at their homes, their workplaces as well as on mobile devices. With increasing visual signals being received by human observers, in the face of degradations that occur to due the capture, compression and transmission processes, an important aspect of the quality of experience of such stimuli is the \emph{perceived visual quality}. This dissertation focuses on algorithm development for assessing such visual quality of natural images, without need for the `pristine' reference image, i.e., we develop computational models for no-reference image quality assessment (NR IQA). Our NR IQA model stems from the theory that natural images have certain statistical properties that are violated in the presence of degradations, and quantifying such deviations from \emph{naturalness} leads to a blind estimate of quality. The proposed modular and easily extensible framework is distortion-agnostic, in that it does not need to have knowledge of the distortion afflicting the image (contrary to most present-day NR IQA algorithms) and is not only capable of quality assessment with high correlation with human perception, but also is capable of identifying the distortion afflicting the image. This additional distortion-identification, coupled with blind quality assessment leads to a framework that allows for blind general-purpose image repair, which is the second major contribution of this dissertation. The blind general-purpose image repair framework, and its exemplar algorithm described here stem from a revolutionary perspective on image repair, where the framework does not simply attempt to ameliorate the distortion in the image, but to ameliorate the distortion, so that visual quality at the output is maximized. Lastly, this dissertation describes a large-scale human subjective study that was conducted at UT to assess human behavior and opinion on visual quality of videos when viewed on mobile devices. The study lead to a database of 200 distorted videos, which incorporates previously studied distortions such as compression and wireless packet-loss, and also dynamically varying distortions that change as a function of time, such as frame-freezes and temporally varying compression rates. This study -- the first of its kind -- involved over 50 human subjects and resulted in 5,300 summary subjective scores and time-sampled subjective traces of quality for multiple displays. The last part of this dissertation analyzes human behavior and opinion on time-varying video quality, opening up an extremely interesting and relevant field for future research in the area of quality assessment and human behavior. / text

This dissertation focuses on the investigation of human perception of stereoscopic 3D image quality and the development of automatic stereoscopic 3D image quality assessment frameworks. In order to assess human perception of visual quality, a human study was conducted and interactions between image quality, depth quality, visual comfort, and 3D viewing quality were inferred. The results indicate that the overall 3D viewing quality can be well predicted from only image quality and depth quality. Between image and depth quality, image quality seems to be the main factor that enables accurate prediction of overall 3D viewing quality. Two other human studies were conducted to study the effect of masking on stereoscopic distortions. Binocular suppression was observed in the stereo images which were distorted by blur, JPEG compression, or JPEG2K compression, however, no such suppression was observed for stereo images distorted by white noise. Further, a facilitation effect was also observed against disparity variation for blur and JPEG2K distorted stereo images while no depth masking effect was observed. Based on these results, I proposed an automatic full-reference (FR) 3D quality assessment framework. In this framework, I used Gabor filterbank responses to model stimulus strength and then synthesize a Cyclopean image from a stereo image pair. Because the quality of this synthesized view is similar to that of a Cyclopean image, which the human visual system recreates from the stereoscopic stimuli, performing the task of 3D quality assessment on synthesized views can deliver better performance. I verified the performance of this FR framework on the LIVE 3D Image Quality Database and the results indicate that applying the proposed framework improves the performance of FR 2D quality assessment algorithms when applied to stereo 3D images. Further, I proposed a no-reference (NR) 3D quality assessment (QA) algorithm based on natural scene statistics in both the spatial and the depth domain. Experiments indicate that the proposed NR algorithm outperforms all 2D FR QA algorithms and most 3D FR QA models in predicting 3D quality of stereo images. Finally, a fourth subjective study was conducted to understand depth quality when stereo content is free from visual discomfort. The result suggests that human perception of depth quality is correlated with the content of the stereo image and the stereoacuity function of human visual system. / text

The overwhelming trend of the usage of multimedia services has raised the consumers' awareness about quality. Both service providers and consumers are interested in the delivered level of perceptual quality. The perceptual quality of an original video signal can get degraded due to compression and due to its transmission over a lossy network. Video quality assessment (VQA) has to be performed in order to gauge the level of video quality. Generally, it can be performed by following subjective methods, where a panel of humans judges the quality of video, or by using objective methods, where a computational model yields an estimate of the quality. Objective methods and specifically No-Reference (NR) or Reduced-Reference (RR) methods are preferable because they are practical for implementation in real-time scenarios. This doctoral thesis begins with a review of existing approaches proposed in the area of NR image and video quality assessment. In the review, recently proposed methods of visual quality assessment are classified into three categories. This is followed by the chapters related to the description of studies on the development of NR and RR methods as well as on conducting subjective experiments of VQA. In the case of NR methods, the required features are extracted from the coded bitstream of a video, and in the case of RR methods additional pixel-based information is used. Specifically, NR methods are developed with the help of suitable techniques of regression using artificial neural networks and least-squares support vector machines. Subsequently, in a later study, linear regression techniques are used to elaborate the interpretability of NR and RR models with respect to the selection of perceptually significant features. The presented studies on subjective experiments are performed using laboratory based and crowdsourcing platforms. In the laboratory based experiments, the focus has been on using standardized methods in order to generate datasets that can be used to validate objective methods of VQA. The subjective experiments performed through crowdsourcing relate to the investigation of non-standard methods in order to determine perceptual preference of various adaptation scenarios in the context of adaptive streaming of high-definition videos. Lastly, the use of adaptive gain equalizer in the modulation frequency domain for speech enhancement has been examined. To this end, two methods of demodulating speech signals namely spectral center of gravity carrier estimation and convex optimization have been studied.

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

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

abstract: The depth richness of a scene translates into a spatially variable defocus blur in the acquired image. Blurring can mislead computational image understanding; therefore, blur detection can be used for selective image enhancement of blurred regions and the application of image understanding algorithms to sharp regions. This work focuses on blur detection and its application to image enhancement. This work proposes a spatially-varying defocus blur detection based on the quotient of spectral bands; additionally, to avoid the use of computationally intensive algorithms for the segmentation of foreground and background regions, a global threshold defined using weak textured regions on the input image is proposed. Quantitative results expressed in the precision-recall space as well as qualitative results overperform current state-of-the-art algorithms while keeping the computational requirements at competitive levels. Imperfections in the curvature of lenses can lead to image radial distortion (IRD). Computer vision applications can be drastically affected by IRD. This work proposes a novel robust radial distortion correction algorithm based on alternate optimization using two cost functions tailored for the estimation of the center of distortion and radial distortion coefficients. Qualitative and quantitative results show the competitiveness of the proposed algorithm. Blur is one of the causes of visual discomfort in stereopsis. Sharpening applying traditional algorithms can produce an interdifference which causes eyestrain and visual fatigue for the viewer. A sharpness enhancement method for stereo images that incorporates binocular vision cues and depth information is presented. Perceptual evaluation and quantitative results based on the metric of interdifference deviation are reported; results of the proposed algorithm are competitive with state-of-the-art stereo algorithms. Digital images and videos are produced every day in astonishing amounts. Consequently, the market-driven demand for higher quality content is constantly increasing which leads to the need of image quality assessment (IQA) methods. A training-free, no-reference image sharpness assessment method based on the singular value decomposition of perceptually-weighted normalized-gradients of relevant pixels in the input image is proposed. Results over six subject-rated publicly available databases show competitive performance when compared with state-of-the-art algorithms. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019

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This diploma thesis deals with objective video quality assessments without reference. Some of the basics of video quality evaluation are described. Also some basic conditions for objective video quality metrics are introduced. The main focus of the thesis are no-reference approaches. Thesis tries to describe basic methods for seeking distortion in video. The difference between spatial-domain oriented and spectral-domain oriented metrics is analyzed. We also describe design of tool for measuring objective video quality in Matlab environment. We then designed and implemented a bit-stream oriented metric for estimation of the PSNR of H.264 coded sequences. Finally, we created a database of video sequences and we held objective tests. Results were compared with results from subjective measurements.