Elements of Data Compression

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Today's increased use of digital sound and video makes data compression crucial to computertechnology because of its vast storage and transmission requirements. The question in many applications is now not whether to compress data, but what compression method should be applied. Most data compression books have been written for professionals and require a strong background in data compression techniques as well as an understanding of algorithms based on sophisticated mathematical models. This book is one of a handful of textbooks to present Data Compression for students in an academic environment. This is not a simple task since most of the widely used algorithms rely on sophisticated mathematical models. Elements of Data Compression addresses the needs of students who will use these techniques on a daily basis. The author accomplishes this through the use of elementary-level representative methods of text, audio, and video compression. Drozdek presents these methods with pseudocode, tables, diagrams, and many worked out examples, all the while employing commonly used techniques that build upon the mathematics students have been exposed to in earlier courses. Key Features The book provides the necessary theoretical foundations for data compression methods. This includes basic concepts of information theory, problems of sampling and quantization, transforms, and filters Where students are likely to need assistance, the author provides extensive auxiliary material on mathematical topics. This material includes some basic facts about matrices, probability distributions, and Fourier analysis The book concentrates on different techniques and algorithms used for data compression today, including a discussion of when these methods should be implemented The algorithms presented in the book are discussed in depth and illustrated with extended worked out examples The book concentrates on the most widely used, preexisting algorithms. Instructors can easily encourage students to implement their variations of the algorithms This material requires the students to have completed a discrete math and a calculus course. Table of Contents 1. Information and Coding 2. Shannon-Fano Coding 3. Huffman Coding 4. Arithmetic Coding 5. Dictionary Methods 6. Sampling And Quantization 7. Predictive Coding 8. Transform Coding 9. Subband Coding 10. Compression of Still Images: jpeg 11. Video Image Compression: MPEG. MPEG-1. MPEG-2. MPEG-4 and MPEG-7 12. Fourier Analysis 13. Wavelets Appendix: Data Compression Software on the Internet