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James A. Storer Computer Science Dept. Brandeis University Waltham, MA 02254 Data compression is the process of encoding a body of data to reduce stor­ age requirements. With Lossless compression, data can be decompressed to be identical to the original, whereas with lossy compression, decompressed data may be an acceptable approximation (according to some fidelity criterion) to the original. For example, with digitized video, it may only be necessary that the decompressed video look as good as the original to the human eye. The two primary functions of data compression are: Storage: The capacity of a storage device can be effectively increased with data compression software or hardware that compresses a body of data on its way to the storage device and decompress it when it is retrieved. Communications: The bandwidth of a digital communication link can be effectively increased by compressing data at the sending end and decom­ pressing data at the receiving end. Here it can be crucial that compression and decompression can be performed in real time.








Content:
Front Matter....Pages i-viii
Front Matter....Pages 1-1
Image Compression and Tree-Structured Vector Quantization....Pages 3-34
Fractal Image Compression Using Iterated Transforms....Pages 35-61
Optical Techniques for Image Compression*....Pages 63-82
Front Matter....Pages 83-83
Practical Implementations of Arithmetic Coding....Pages 85-112
Context Modeling for Text Compression....Pages 113-144
Ziv-Lempel Compressors with Deferred-Innovation....Pages 145-157
Massively Parallel Systolic Algorithms for Real-Time Dictionary-Based Text Compression....Pages 159-178
Front Matter....Pages 179-179
Variations on a Theme by Gallager....Pages 181-213
On the Coding Delay of a General Coder....Pages 215-252
Finite State Two-Dimensional Compressibility....Pages 253-275
Back Matter....Pages 277-354



Content:
Front Matter....Pages i-viii
Front Matter....Pages 1-1
Image Compression and Tree-Structured Vector Quantization....Pages 3-34
Fractal Image Compression Using Iterated Transforms....Pages 35-61
Optical Techniques for Image Compression*....Pages 63-82
Front Matter....Pages 83-83
Practical Implementations of Arithmetic Coding....Pages 85-112
Context Modeling for Text Compression....Pages 113-144
Ziv-Lempel Compressors with Deferred-Innovation....Pages 145-157
Massively Parallel Systolic Algorithms for Real-Time Dictionary-Based Text Compression....Pages 159-178
Front Matter....Pages 179-179
Variations on a Theme by Gallager....Pages 181-213
On the Coding Delay of a General Coder....Pages 215-252
Finite State Two-Dimensional Compressibility....Pages 253-275
Back Matter....Pages 277-354
....
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