Hyperspectral Data Compression provides a survey of recent results in the field of compression of remote sensed 3D data, with a particular interest in hyperspectral imagery. Chapter 1 addresses compression architecture, and reviews and compares compression methods. Chapters 2 through 4 focus on lossless compression (where the decompressed image must be bit for bit identical to the original). Chapter 5, contributed by the editors, describes a lossless algorithm based on vector quantization with extensions to near lossless and possibly lossy compression for efficient browning and pure pixel classification. Chapter 6 deals with near lossless compression while. Chapter 7 considers lossy techniques constrained by almost perfect classification. Chapters 8 through 12 address lossy compression of hyperspectral imagery, where there is a tradeoff between compression achieved and the quality of the decompressed image. Chapter 13 examines artifacts that can arise from lossy compression.

This book presents exciting recent research on the compression of images and text. Part 1 presents the (lossy) image compression techniques of vector quantization, iterated transforms (fractal compression), and techniques that employ optical hardware. Part 2 presents the (lossless) text compression techniques of arithmetic coding, context modeling, and dictionary methods (LZ methods); this part of the book also addresses practical massively parallel architectures for text compression. Part 3 presents theoretical work in coding theory that has applications to both text and image compression. The book ends with an extensive bibliography of data compression papers and books which can serve as a valuable aid to researchers in the field. Points of Interest: Data compression is becoming a key factor in the digital storage of text, speech graphics, images, and video, digital communications, data bases, and supercomputing. The book addresses hot' data compression topics such as vector quantization, fractal compression, optical data compression hardware, massively parallel hardware, LZ methods, arithmetic coding. Contributors are all accomplished researchers. Extensive bibliography to aid researchers in the field.

This book presents exciting recent research on the compression of images and text. Part 1 presents the (lossy) image compression techniques of vector quantization, iterated transforms (fractal compression), and techniques that employ optical hardware. Part 2 presents the (lossless) text compression techniques of arithmetic coding, context modeling, and dictionary methods (LZ methods); this part of the book also addresses practical massively parallel architectures for text compression. Part 3 presents theoretical work in coding theory that has applications to both text and image compression. The book ends with an extensive bibliography of data compression papers and books which can serve as a valuable aid to researchers in the field. Points of Interest: * Data compression is becoming a key factor in the digital storage of text, speech graphics, images, and video, digital communications, data bases, and supercomputing. * The book addresses 'hot' data compression topics such as vector quantization, fractal compression, optical data compression hardware, massively parallel hardware, LZ methods, arithmetic coding. * Contributors are all accomplished researchers.* Extensive bibliography to aid researchers in the field.

Hyperspectral Data Compression provides a survey of recent results in the field of compression of remote sensed 3D data, with a particular interest in hyperspectral imagery. Chapter 1 addresses compression architecture, and reviews and compares compression methods. Chapters 2 through 4 focus on lossless compression (where the decompressed image must be bit for bit identical to the original). Chapter 5, contributed by the editors, describes a lossless algorithm based on vector quantization with extensions to near lossless and possibly lossy compression for efficient browning and pure pixel classification. Chapter 6 deals with near lossless compression while. Chapter 7 considers lossy techniques constrained by almost perfect classification. Chapters 8 through 12 address lossy compression of hyperspectral imagery, where there is a tradeoff between compression achieved and the quality of the decompressed image. Chapter 13 examines artifacts that can arise from lossy compression.