This book provides readers with a comprehensive review of image quality assessment technology, particularly applications on screen content images, 3D-synthesized images, sonar images, enhanced images, light-field images, VR images, and super-resolution images. It covers topics containing structural variation analysis, sparse reference information, multiscale natural scene statistical analysis, task and visual perception, contour degradation measurement, spatial angular measurement, local and global assessment metrics, and more. All of the image quality assessment algorithms of this book have a high efficiency with better performance compared to other image quality assessment algorithms, and the performance of these approaches mentioned above can be demonstrated by the results of experiments on real-world images. On the basis of this, those interested in relevant fields can use the results obtained through these quality assessment algorithms for further image processing. The goal of this book is to facilitate the use of these image quality assessment algorithms by engineers and scientists from various disciplines, such as optics, electronics, math, photography techniques and computation techniques. The book can serve as a reference for graduate students who are interested in image quality assessment techniques, for front-line researchers practicing these methods, and for domain experts working in this area or conducting related application development.

This book provides comprehensive coverage of the latest trends/advances in subjective and objective quality evaluation for traditional visual signals, such as 2D images and video, as well as the most recent challenges for the field of multimedia quality assessment and processing, such as mobile video and social media. Readers will learn how to ensure the highest storage/delivery/ transmission quality of visual content (including image, video, graphics, animation, etc.) from the server to the consumer, under resource constraints, such as computation, bandwidth, storage space, battery life, etc.

The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also encompasses the preliminary knowledge of Machine Learning (ML) to VQA tasks and newly developed ML techniques for the purpose. Hence, firstly, it is particularly helpful to the beginner-readers (including research students) to enter into VQA field in general and LB-VQA one in particular. Secondly, new development in VQA and LB-VQA particularly are detailed in this book, which will give peer researchers and engineers new insights in VQA.

Last few years have seen rapid acceptance of high-definition television (HDTV) technology around the world. This technology has been hugely successful in delivering more realistic television experience at home and accurate imaging for professional applications. Adoption of high definition continues to grow as consumers demand enhanced features and greater quality of content. Following this trend, natural evolution of visualisation technologies will be in the direction of fully realistic visual experience and highly precise imaging. However, using the content of even higher resolution and quality is not straightforward as such videos require significantly higher access bandwidth and more processing power. Therefore, methods for radical reduction of video bandwidth are crucial for realisation of high visual quality. Moreover, it is desirable to look into other ways of accessing visual content, solution to which lies in innovative schemes for content delivery and consumption. This book presents selected chapters covering technologies that will enable greater flexibility in video content representation and allow users to access content from any device and to interact with it.

In recent years visual devices have proliferated, from the massive high-resolution, high-contrast screens to the tiny ones on mobile phones, with their limited dynamic range and color gamut. The wide variety of screens on which content may be viewed creates a challenge for developers. Adapting visual content for optimized viewing on all devices is called retargeting. This is the first book to provide a holistic view of the subject, thoroughly reviewing and analyzing the many techniques that have been developed for retargeting along dimensions such as color gamut, dynamic range, and spatial resolution.

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

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.