Handbook of Data Compression by David Salomon, Giovanni Motta, D. Bryant

By David Salomon, Giovanni Motta, D. Bryant

Data compression is likely one of the most crucial fields and instruments in smooth computing. From archiving facts, to CD-ROMs, and from coding idea to picture research, many aspects of contemporary computing depend upon information compression. This ebook presents a complete reference for the numerous differing kinds and strategies of compression. incorporated are a close and worthy taxonomy, research of commonest equipment, and discussions at the use and comparative advantages of equipment and outline of "how to" use them. particular descriptions and causes of the main famous and often used compression equipment are lined in a self-contained type, with an obtainable type and technical point for experts and non-specialists.

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The few compression curiosities that appear here are from [curiosities 08]. ” When it comes to compressing data, we all want the best compressor. So, how much can a file possibly be compressed? Lossless methods routinely compress files to less than half their size and can go down to compression ratios of about 1/8 or smaller. Lossy algorithms do much better. Is it possible to compress a file by a factor of 10,000? Now that would be a curiosity. 39) can compress certain files by factors of thousands.

Entropy is your goal, strive to achieve it. 3. Read the literature before you try to publish/implement your new, clever compression algorithm. Others may have been there before you. 4. There is no universal compression method that can compress any file to just a few bytes. Thus, refrain from making incredible claims. They will come back to haunt you. 5. The G-d of compression prefers free and open source codecs. 6. If you decide to patent your algorithm, make sure anyone can understand your patent application.

A value of 60 means that the output stream occupies 40% of its original size (or that the compression has resulted in savings of 60%). Introduction 13 4. In image compression, the quantity bpp (bits per pixel) is commonly used. It equals the number of bits needed, on average, to compress one pixel of the image. This quantity should always be compared with the bpp before compression. 5. The compression gain is defined as 100 loge reference size , compressed size where the reference size is either the size of the input stream or the size of the compressed stream produced by some standard lossless compression method.

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