Browsing by Subject "Quality assessment"
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Item Applied statistical modeling of three-dimensional natural scene data(2014-05) Su, Che-Chun; Bovik, Alan C. (Alan Conrad), 1958-; Cormack, Lawrence K.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.Item Information theoretic methods in distributed compression and visual quality assessment(2012-05) Soundararajan, Rajiv; Bovik, Alan C. (Alan Conrad), 1958-; Vishwanath, Sriram; Geisler, Wilson; Ghosh, Joydeep; de Veciana, Gustavo; Vikalo, HarisDistributed compression and quality assessment (QA) are essential ingredients in the design and analysis of networked signal processing systems with voluminous data. Distributed source coding techniques enable the efficient utilization of available resources and are extremely important in a multitude of data intensive applications including image and video. The quality analysis of such systems is also equally important in providing benchmarks on performance leading to improved design and control. This dissertation approaches the complementary problems of distributed compression and quality assessment using information theoretic methods. While such an approach provides intuition on designing practical coding schemes for distributed compression, it directly yields image and video QA algorithms with excellent performance that can be employed in practice. This dissertation considers the information theoretic study of sophisticated problems in distributed compression including, multiterminal multiple description coding, multiterminal source coding through relays and joint source channel coding of correlated sources over wireless channels. Random and/or structured codes are developed and shown to be optimal or near optimal through novel bounds on performance. While lattices play an important role in designing near optimal codes for multiterminal source coding through relays and joint source channel coding over multiple access channels, time sharing random Gaussian codebooks is optimal for a wide range of system parameters in the multiterminal multiple description coding problem. The dissertation also addresses the challenging problem of reduced reference image and video QA. A family of novel reduced reference image and video QA algorithms are developed based on spatial and temporal entropic differences. While the QA algorithms for still images only compute spatial entropic differences, the video QA algorithms compute both spatial and temporal entropic differences and combine them in a perceptually relevant manner. These algorithms attain excellent performances in terms of correlation with human judgments of quality on large QA databases. The framework developed also enables the study of the degradation in performance of QA algorithms from full reference information to almost no information from the reference image or video.Item Natural scene statistics-based blind visual quality assessment in the spatial domain(2013-05) Mittal, Anish; Bovik, Alan C. (Alan Conrad), 1958-With the launch of networked handheld devices which can capture, store, compress, send and display a variety of audiovisual stimuli; high definition television (HDTV); streaming Internet protocol TV (IPTV) and websites such as Youtube, Facebook and Flickr etc., an enormous amount of visual data of visual data is making its way to consumers. Because of this, considerable time and resources are being expanded to ensure that the end user is presented with with a satisfactory quality of experience (QoE). While traditional QoE methods have focused on optimizing delivery networks with respect to throughput, buffer-lengths and capacity, perceptually optimized delivery of multimedia services is also fast gaining importance. This is especially timely given the explosive growth in (especially wireless) video traffic and expected shortfalls in bandwidth. These perceptual approaches attempt to deliver an optimized QoE to the end-user by utilizing objective measures of visual quality. In this thesis, we shall cover a variety of such algorithms that predict overall QoE of an image or a video, depending on the amount of information available for the algorithm design. Typically, quality assessment (QA) algorithms are classiffied on the basis of the amount of information that is available to the algorithm. This thesis will primarily focus on blind QA algorithms, where blind or no-reference (NR) QA refers to automatic quality assessment of an image/video using an algorithm which only utilizes the distorted image/video whose quality is being assessed. NR QA approaches are further classiffied on the basis of whether the algorithm had access to subjective/human opinion prior to deployment. Algorithms which use machine learning techniques along with human judgements of quality during the 'training' phase may be labelled 'opinion aware' algorithms. The first part of the thesis deals with such approaches. While such opinion aware-NR algorithms demonstrate good correlation with human perception on controlled databases, it is impossible to anticipate all of the different distortions that may occur in a practical system and hence train on them. In such cases, it is of interest to design QA algorithms that are not limited in their performance by training data. Approaches which operate without the knowledge of human judgements during the training phase are labelled as 'opinion unaware' (OU) algorithms. We propose such an approach in the second part of the thesis. Further, we propose new VQA algorithms in the last part of the dissertation to address the completely blind VQA problem. The proposed approach quantify disturbances introduced due to distortions and thereby predict the quality of distorted content even without any external knowledge about the pristine natural sources and hence zero shot models.