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Total Size:
26.7 MB
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3B4EBEC80C9658712963675D72AB812F10120D78
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Aug. 21, 2025, 3:01 p.m.
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(Last updated: Aug. 21, 2025, 3:06 p.m.)
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| Wei G. AI-based Image and Video Coding. Methods, Standards,and Applications 2025.pdf | 26.7 MB |
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NOTE
SOURCE: Wei G. AI-based Image and Video Coding. Methods, Standards,and Applications 2025
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COVER
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MEDIAINFO
Textbook in PDF format Unlocking the future of image and video coding with AI-based Image and Video Coding: Methods, Standards, and Applications, we can explore the revolutionary impact of Deep Learning technologies on image and video compression. This book is a must-read for researchers, practitioners, and students eager to stay ahead of the curve in an ever-evolving field where Deep Learning and Artificial Intelligence (AI) are setting new benchmarks in coding efficiency. With an unparalleled focus on the intersection of Deep Learning and multimedia coding, this book offers cutting-edge insights into the latest techniques and standards driving progress in the industry. From the core principles of coding technologies to advanced topics like 3D and multimodal coding, human and machine vision-oriented compression, and compression artifacts removal, it covers all the related essentials. Special attention is given to the practical aspects of implementations, open-source projects, and standardization efforts from leading organizations like IEEE, JPEG, MPEG and MPAI. Whether you are a scholar, a professional in the multimedia industry, or a student with a foundation in Computer Science and electrical engineering, this book equips you with the fundamental knowledge and tools to master the latest advancements in AI-based image and video coding. You can gain a deeper understanding of how Deep Learning reshapes the future of image and video coding, and explore new possibilities of optimizing compression performances for both humans and machines. This book systematically investigates the image and video data coding approaches driven by the fast-growing Artificial Intelligence (AI) technologies, structured through 12 interconnected chapters. The organizational logic progresses from foundational principles to implementation challenges, adhering to three core objectives: (1) To establish theoretical bridges between information theory and neural compression methods. (2) To analyze architectural innovations surpassing the conventional codec performance boundaries. (3) To address practical deployment considerations in standardization and application systems. This book illustrate the theories, methods, and applications of AI-based image and video coding. It provides a comprehensive knowledge of image and video coding powered with AI, including fundamental principles, human visual perception modeling, Deep Learning-based coding methods, advanced coding problems, technical standards, implementations and open-source projects, and future works. The detailed chapters are organized as follows. Chapters 1 and 2 begin with an introduction that lays the foundation for the book. In particular, Chap. 1 outlines the core concepts of image and video coding, and Chap. 2 performs a systematic analysis of the Deep Learning theoretical framework highly relevant to this research field. Chapter 3 introduces the fundamentals of quality assessment and perceptual modeling, which are essential in evaluating the image and video coding performances, and guiding perceptual optimization in coding processes. Chapters 4 and 5 systematically investigate the utilization of deep neural networks in image and video coding, respectively, presenting the methodologies of using devised techniques to improve coding efficiency. Chapters 6–8 discuss several advanced coding problems, focusing on 3D and multimodal coding, human and machine perception oriented coding, and compression artifacts removal, respectively. Chapter 9 presents the recent standardization efforts on image and video coding from the working groups of different organizations, e.g., IEEE, MPEG, and JPEG, as well as the comparative analysis of emerging standards. Chapter 10 provides the basic knowledge for implementing AI-based image and video coding techniques, including insights into hardware and software considerations. Chapter 11 gives a review of valuable open source projects on image and video coding. Chapter 12 looks to the future, elaborating on potential directions for research and development in AI-based image and video coding technologies. These chapters constitute the whole book, including the fundamental principles and advanced methods for image and video coding with the aid of Deep Learning. We aim to provide readers with the indispensable knowledge and tools they need to contribute to this dynamic and evolving field, and also aim to accelerate the transition from the theoretical neural compression models to industrially deployable solutions
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