Fundamental Data Compression
Fundamental Data Compression provides all the information students need to be able to use this essential technology in their future careers. A huge, active research field, and a part of many people's everyday lives, compression technology is an essential part of today's Computer Science and Electronic Engineering courses.
With the help of this book, students can gain a thorough understanding of the underlying theory and algorithms, as well as specific techniques used in a range of scenarios, including the application of compression techniques to text, still images, video and audio. Practical exercises, projects and exam questions reinforce learning, along with suggestions for further reading.
* Dedicated data compression textbook for use on undergraduate courses
* Provides essential knowledge for today's web/multimedia applications
* Accessible, well structured text backed up by extensive exercises and sample exam questions
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Chapter 10 Image compression
Chapter 11 Video compression
Appendix A Brief history
Appendix B Matrices
Appendix C Fourier series and harmonic analysis
Appendix D Pseudocode notation
Appendix E Notation
Chapter 9 Audio compression
ABCDEFG adaptive Huffman coding algorithm design alphabet applied approach arithmetic coding array ASCII audio average length bi-level images binary tree bitmap image bytes called Chapter characters coder codeword codeword lengths colour compressed file compression algorithm compression methods compression ratio compression techniques current Interval data compression decoding algorithm decoding process decompression derived dictionary efficiency entropy entry example Figure fixed length frames frequency function greyscale image History buffer Huffman encoding Huffman tree image compression implementation input symbol iteration JPEG KKKKKKKKK Kraft inequality lossless compression lossy compression match matrix minimum-variance next_character non-run number of bits original string output pixels prediction prefix code probability distribution quantisation reconstructed redundancy represent run-length algorithm sample self-information sequence of symbols Shannon–Fano signal source data source file step subinterval symbolic data token transform uniquely decodable updated values variable length code wavelet word word+x