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With multimedia research burgeoning, video applications have become essential to our daily life. However, as the compression becomes more aggressive, too much data loss results in degrading perceived video quality for viewers. Therefore, an accurate quality measurement is important to improve or preserve the quality of compressed video. This dissertation focuses on measuring the quality degradations that are caused by compression. We specifically target distortions with impact above the human perceptual threshold, which are also called artifacts. This type of distortion usually appears in a structured form. This characteristic makes quality assessment highly content dependent and many existing metrics fail in this regard. Some previous research has tried to raise the accuracy of video quality assessment by considering human visual system (HVS) effects, or human visual attention factors. However, both HVS and human visual attention have very strong interaction in the video quality assessment process, and none of the existing quality measurement research takes both of them into account. In addition, cognitive factors significantly influence the visual quality assessment process, but they have been ignored in current quality assessment research. Based on these realizations, a new video quality assessment philosophy is introduced in this thesis. It considers the characteristics of artifacts, effects from HVS, visual attention, and cognitive non-linearity. First, a new human visual module is proposed, it takes both visual masking and attention effects into account. Its unique design makes embedding this visual module in any video quality related applications very easy. Based on this new human visual module, a blurriness metric is designed which includes cognitive characteristics. This new blurriness metric does not rely on edge information, and is more robust at assessing heavily compressed video data. A metric for artifacts introduced by motion compensated field interpolation (MCFI) is also implemented. It is the first metric ever designed for measuring the spatial quality of temporally interpolated frames. From a temporal quality perspective, a novel temporal quality metric is designed to measure the temporal quality degradation caused by both uniform and non-uniform distributed frame loss. Experimental data shows these metrics significantly outperform the existing metrics.
This book has brought 24 groups of experts and active researchers around the world together in image processing and analysis, video processing and analysis, and communications related processing, to present their newest research results, exchange latest experiences and insights, and explore future directions in these important and rapidly evolving areas. It aims at increasing the synergy between academic and industry professionals working in the related field. It focuses on the state-of-the-art research in various essential areas related to emerging technologies, standards and applications on analysis, processing, computing, and communication of multimedia information. The target audience of this book is researchers and engineers as well as graduate students working in various disciplines linked to multimedia analysis, processing and communications, e.g., computer vision, pattern recognition, information technology, image processing, and artificial intelligence. The book is also meant to a broader audience including practicing professionals working in image/video applications such as image processing, video surveillance, multimedia indexing and retrieval, and so on. We hope that the researchers, engineers, students and other professionals who read this book would find it informative, useful and inspirational toward their own work in one way or another.
Because of the increasing ease of image and video capture, many millions of consumers create and upload large volumes of User-Generated-Content (UGC) images and videos to social and streaming media sites over the Internet. UGC images and videos are commonly captured by naive users having limited skills and imperfect techniques, and tend to be afflicted by mixtures of highly diverse in-capture distortions. They are then often uploaded for sharing onto cloud servers, where they are further compressed for storage and transmission. My Ph.D. research first tackles the highly practical problem of predicting the quality of compressed images and videos with only (possibly severely) distorted UGC references. To address this problem, we develop a novel two-step image quality prediction concept called 2stepQA, and a novel Video Quality Assessment (VQA) framework called 1stepVQA. We construct a new, first-of-a-kind dedicated image quality database specialized for the design and testing of two-step IQA models, and a new dedicated video database, which was created by applying a realistic VMAF-Guided perceptual rate distortion optimization (RDO) criterion to create realistically compressed versions of UGC source videos, which typically have pre-existing distortions. Furthermore, we also study the automatic quality prediction of a particular UGC category, UGC gaming videos. To do this, we create a novel UGC gaming video resource, called the LIVE-YouTube Gaming video quality (LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. We create a new VQA model specifically designed to succeed on UGC gaming videos, called the Gaming Video Quality Predictor (GAME-VQP). GAME-VQP successfully predicts the unique statistical characteristics of gaming videos by drawing upon features designed under modified natural scene statistics models, combined with gaming specific features learned by a Convolution Neural Network. We study the performance of 2stepQA, 1stepVQA, and GAME-VQP on the three new video (image) databases, respectively, and find that they all outperform other mainstream VQA models
This Lecture book is about objective image quality assessment—where the aim is to provide computational models that can automatically predict perceptual image quality. The early years of the 21st century have witnessed a tremendous growth in the use of digital images as a means for representing and communicating information. A considerable percentage of this literature is devoted to methods for improving the appearance of images, or for maintaining the appearance of images that are processed. Nevertheless, the quality of digital images, processed or otherwise, is rarely perfect. Images are subject to distortions during acquisition, compression, transmission, processing, and reproduction. To maintain, control, and enhance the quality of images, it is important for image acquisition, management, communication, and processing systems to be able to identify and quantify image quality degradations. The goals of this book are as follows; a) to introduce the fundamentals of image quality assessment, and to explain the relevant engineering problems, b) to give a broad treatment of the current state-of-the-art in image quality assessment, by describing leading algorithms that address these engineering problems, and c) to provide new directions for future research, by introducing recent models and paradigms that significantly differ from those used in the past. The book is written to be accessible to university students curious about the state-of-the-art of image quality assessment, expert industrial R&D engineers seeking to implement image/video quality assessment systems for specific applications, and academic theorists interested in developing new algorithms for image quality assessment or using existing algorithms to design or optimize other image processing applications.
Visual quality assessment is an interdisciplinary topic that links image/video processing, psychology and physiology. Many engineers are familiar with the image/video processing; transmission networks side of things but not with the perceptual aspects pertaining to quality. Digital Video Quality first introduces the concepts of human vision and visual quality. Based on these, specific video quality metrics are developed and their design is presented. These metrics are then evaluated and used in a number of applications, including image/video compression, transmission and watermarking. Introduces the concepts of human vision and vision quality. Presents the design and development of specific video quality metrics. Evaluates video quality metrics in the context of image/video compression, transmission and watermarking. Presents tools developed for the analysis of video quality
Intelligent Image and Video Compression: Communicating Pictures, Second Edition explains the requirements, analysis, design and application of a modern video coding system. It draws on the authors’ extensive academic and professional experience in this field to deliver a text that is algorithmically rigorous yet accessible, relevant to modern standards and practical. It builds on a thorough grounding in mathematical foundations and visual perception to demonstrate how modern image and video compression methods can be designed to meet the rate-quality performance levels demanded by today's applications and users, in the context of prevailing network constraints. "David Bull and Fan Zhang have written a timely and accessible book on the topic of image and video compression. Compression of visual signals is one of the great technological achievements of modern times, and has made possible the great successes of streaming and social media and digital cinema. Their book, Intelligent Image and Video Compression covers all the salient topics ranging over visual perception, information theory, bandpass transform theory, motion estimation and prediction, lossy and lossless compression, and of course the compression standards from MPEG (ranging from H.261 through the most modern H.266, or VVC) and the open standards VP9 and AV-1. The book is replete with clear explanations and figures, including color where appropriate, making it quite accessible and valuable to the advanced student as well as the expert practitioner. The book offers an excellent glossary and as a bonus, a set of tutorial problems. Highly recommended! --Al Bovik An approach that combines algorithmic rigor with practical implementation using numerous worked examples Explains how video compression methods exploit statistical redundancies, natural correlations, and knowledge of human perception to improve performance Uses contemporary video coding standards (AVC, HEVC and VVC) as a vehicle for explaining block-based compression Provides broad coverage of important topics such as visual quality assessment and video streaming
Multimedia Quality of Experience (QoE) Current Status and Future Requirements Multimedia Quality of Experience (QoE): Current Status and Future Requirements discusses the current status of QoE (Quality of Experience) research, providing guidelines on QoE assessment and management practice. Moreover, it covers many different aspects of QoE research, including definitions, standardization (ITU, ETSI, IEEE, IETF), measurement, management, and architectures. In addition, the authors bring together contributions from recognized experts (worldwide) in the area of subjective and objective QoE video assessment. Discusses the current status of QoE research; reporting the latest advances from various standardization bodies Provides guidelines on QoE assessment and management practice Explores methods, means, and architectures of QoE Considers future requirements of QoE
The hand is quicker than the eye. In many cases, so is digital video. Maintaining image quality in bandwidth- and memory-restricted environments is quickly becoming a reality as thriving research delves ever deeper into perceptual coding techniques, which discard superfluous data that humans cannot process or detect. Surveying the topic from a Human Visual System (HVS)-based approach, Digital Video Image Quality and Perceptual Coding outlines the principles, metrics, and standards associated with perceptual coding, as well as the latest techniques and applications. This book is divided broadly into three parts. First, it introduces the fundamental theory, concepts, principles, and techniques underlying the field, such as the basics of compression, HVS modeling, and coding artifacts associated with current well-known techniques. The next section focuses on picture quality assessment criteria; subjective and objective methods and metrics, including vision model based digital video impairment metrics; testing procedures; and international standards regarding image quality. Finally, practical applications come into focus, including digital image and video coder designs based on the HVS as well as post-filtering, restoration, error correction, and concealment techniques. The permeation of digital images and video throughout the world cannot be understated. Nor can the importance of preserving quality while using minimal storage space, and Digital Video Image Quality and Perceptual Coding provides the tools necessary to accomplish this goal. Instructors and lecturers wishing to make use of this work as a textbook can download a presentation of 786 slides in PDF format organized to augment the text. accompany our book (H.R. Wu and K.R. Rao, Digital Video Image Quality and Perceptual Coding, CRC Press (ISBN: 0-8247-2777-0), Nov. 2005) for lecturers or instructor to use for their classes if they use the book.