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Barni M. (ed.) Document and Image Compression

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Barni M. (ed.) Document and Image Compression
Издательство CRC Press, 2006. — 456 p.
Still image coding is one of the oldest research topics in the image processing field. Activity in this area has spanned the last four decades, producing a huge volume of scientific literature and an innumerable amount of algorithms and techniques, each with their own merits and drawbacks. Starting from the mid-1980s, an intense standardization activity has been carried out, leading to the release of the worldwide popular JPEG standard in 1992. Since then, the JPEG format has reached an ubiquitous diffusion, becoming the privileged means to exchange images stored in digital format.
Along with JPEG, other coding standards have been developed, like the JBIG standard, just to mention one, that meet the requirements set by particular applications. Spurred by new advances in image processing and the availability of increasing computational power, a new standardization activity began in the mid-1990s that resulted in the release of two new coding standards: the JPEG-LS and JPEG 2000. Image coding is still a mature research field, leading one to assume that little room exists for new significant advances. In this framework, a question naturally arises: Is image coding research still worth it? As strange as it may seem, the answer is definitely yes. The reasons for such an answer are rather simple and are basically the same as those that spurred the research in the last decades: The computing power made available by technological advancements is continuously increasing, hence making it possible for the development of new approaches that were unfeasible until a few years ago. At the same time, new image representation tools are being developed whose exploitation for image coding looks particularly promising. Finally, new application areas are appearing that call for the development of ad hoc image coding algorithms.
Having answered the above basic question about the usefulness of image coding research, it is also possible to answer a second question: Is a new book on still image coding worth it? Once again the answer is yes. One reason is that the process that led to the abandonment of the old block-DCT approach that is typical of the JPEG standard is not properly covered by most image coding textbooks. Second, new promising research directions are still appearing, making image coding research as exciting as it has been during the last decades. Finally, while many books deal with the application of image coding to a particular application scenario, it is not easy to find a reference in which several such scenarios are gathered together. It is the aim of this book to provide a snapshot of the latest advancements in the image coding field, providing an up-to-date reference of the best performing schemes proposed so far, while at the same time highlighting some new research directions that are likely to assume great importance in the years to come. In this spirit, this book is targeted to scientists and practitioners who, while possessing an elementary knowledge of conventional image coding theory including basic notions of image processing and information theory, want to keep abreast of the latest results in the field.
The book is organized into three parts. Part I describes the current state of the art of image coding. The first three chapters of this part are of a general nature. Chapter 1 (Multiresolution Analysis for Image Compression, by Alparone, Argenti, and Bianchi) provides an introduction to multiresolution image representation and its application to image coding, with multiresolution analysis being the common denominator of most of the post-JPEG image coding algorithms. Chapter 2 (Advanced Modeling and Coding Techniques for Image Compression by Taubman) presents advanced coding and modeling techniques that are applied in some of the newest image compression algorithms. Chapter 3 (PerceptualAspects of Image Coding, by Neri, Carli, and Mitra) deals with perceptual issues in image coding, a topic that has received renewed interest in recent years. The next two chapters focus on two classes of coding schemes; the former (The JPEG Family of Coding Standards, by Magli) presents the JPEG family of coding standards with particular attention to JPEG 2000 and the latter (Lossless Image Coding, by Forchhammer and Memon) discusses lossless image coding, a topic that has also received increased interest in recent years due to its importance in applications like remote sensing and biomedical imaging. The first part of the book ends with a chapter on fractal image coding (Fractal Image Compression, by Hamzaoui and Saupe), a completely different approach to the coding problem that was developed during the 1990s.
The second part of the book introduces the reader to four new research fields: image coding by means of image representation paradigms that go beyond the wavelet-based framework (Beyond Wavelets: NewImage Representation Paradigms, by Fьhr, Demaret, and Friedrich); image coding by means of redundant dictionaries, an approach whose interest is going to increase with the availability of increasing computing power (Image Coding Using Redundant Dictionaries, by Vandergheynst and Frossard); application of distributed source coding paradigms to image compression (Distributed Compression of Field Snapshots in Sensor Networks, by Servetto); and the exploitation of novel datahiding techniques to improve the effectiveness of current codecs both in terms of coding efficiency and robustness (Data Hiding for Image and Video Coding, by Campisi and Piva).
The third and last part of the book is devoted to the description of coding techniques expressly developed to compress particular classes of images, to which the general-purpose algorithms could not be applied successfully. In particular, Chapter 11 (Binary Image Compression, by Boncelet) discusses the compression of binary images, Chapter 12 (Two-Dimensional Shape Coding, by Ostermann and Vetro) deals with the coding of binary shapes, and Chapter 13 (Compressing Compound Documents, by de Queiroz) presents coding schemes available to efficiently compress compound documents, i.e., images depicting both natural scenes and text. In Chapter 14 (Trends in Model-Based Coding of Multidimensional Medical Data, by Menegaz) the compression of biomedical images is addressed, whereas in Chapter 15 (Remote-Sensing Image Coding, by Aiazzi, Baronti, and Lastri) coding of remote sensing imagery is considered. The book ends with a chapter (Lossless Compression of VLSI Layout Image Data, by Dai and Zakhor) devoted to the compression of VLSI image data. As is evident from the outline given above, this book is the result of the efforts of several researchers spread all over the world. I am grateful to all of them for agreeing to share their expertise and time. Without them, this book would have never been possible. I also thank my wife, Francesca, and my sons, Giacomo, Margherita, and Simone; even if they did not actively participate in the preparation of the book, their closeness has been an invaluable help to me. Finally, I would like to thank my colleague and friend, Franco Bartolini, who suddenly passed away on January 1, 2004 at a young age of
39. As with many other initiatives, we conceived this book together, though in the end Franco was not able to accompany me in this project. There is still much of his view in it.
State of the Art.
Multiresolution Analysis for Image Compression.
Advanced Modeling and Coding Techniques for Image Compression.
Perceptual Aspects of Image Coding.
The JPEG Family of Coding Standards.
Lossless Image Coding.
Fractal Image Compression.
New Directions.
BeyondWavelets: New Image Representation Paradigms.
Image Coding Using Redundant Dictionaries.
Distributed Compression of Field Snapshots in Sensor Networks.
Data Hiding for Image and Video Coding.
Domain-Specific Coding.
Binary Image Compression.
Two-Dimensional Shape Coding.
Compressing Compound Documents.
Trends in Model-Based Coding of Multidimensional Medical Data.
Remote-Sensing Image Coding.
Lossless Compression of VLSI Layout Image Data.
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