Image and Text Compression

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.

Inhalt

Introduction. Part 1: Image Compression. 1. Image Compression and Tree-Structured Vector Quantization; R.M. Gray, P.C. Cosman, E.A. Riskin. 2. Fractal Image Compression Using Itereated Transforms; Y. Fisher, E.W. Jacobs, R.D. Boss. 3. Optical Techniques for Image Compression; J.H. Reif, A. Yoshida. Part 2: Text Compression. 4. Practical Implementations of Arithmetic Coding; P.G. Howard, J.S. Vitter. 5. Context Modeling for Text Compression; D.S. Hirschberg, D.A. Lelewer. 6. Ziv-Lempel Compressors with Deferred-Innovation; M. Cohn. 7. Massively Parallel Systolic Algorithms for Real-Time Dictionary-Based Text Compression; J.A. Storer. Part 3: Coding Theory. 8. Variations on a Theme by Gallager; R.-M. Capocelli, A. De Santis. 9. On the Coding Delay of a General Coder; M.J. Weinberger, A. Lempel, J. Ziv. 10. Finite State Two-Dimensional Compressibility; D. Sheinwald. Bibliography. Index.