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Compression ratios are around 50–60% of the original size, [49] which is similar to those for generic lossless data compression. Lossless codecs use curve fitting or linear prediction as a basis for estimating the signal. Parameters describing the estimation and the difference between the estimation and the actual signal are coded separately.
The Canterbury corpus is a collection of files intended for use as a benchmark for testing lossless data compression algorithms. It was created in 1997 at the University of Canterbury, New Zealand and designed to replace the Calgary corpus. The files were selected based on their ability to provide representative performance results. [1]
The most widely used lossy compression algorithm is the discrete cosine transform (DCT), first published by Nasir Ahmed, T. Natarajan and K. R. Rao in 1974. Lossy compression is most commonly used to compress multimedia data (audio, video, and images), especially in applications such as streaming media and internet telephony. By contrast ...
For example, uncompressed songs in CD format have a data rate of 16 bits/channel x 2 channels x 44.1 kHz ≅ 1.4 Mbit/s, whereas AAC files on an iPod are typically compressed to 128 kbit/s, yielding a compression ratio of 10.9, for a data-rate saving of 0.91, or 91%.
Sami Runsas (the author of NanoZip) maintained Compression Ratings, a benchmark similar to Maximum Compression multiple file test, but with minimum speed requirements. It offered the calculator that allowed the user to weight the importance of speed and compression ratio. The top programs were fairly different due to the speed requirement.
The Weissman score is a performance metric for lossless compression applications. It was developed by Tsachy Weissman, a professor at Stanford University, and Vinith Misra, a graduate student, at the request of producers for HBO's television series Silicon Valley, a television show about a fictional tech start-up working on a data compression algorithm.
CRUNCH's implementation of LZW had a somewhat unusual feature of modifying and occasionally clearing the code table in memory when it became full, resulting in a few percent better compression on many files. .xz application/x-xz xz: Unix-like A compression format using LZMA2 to yield high compression ratios.
Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic data compression methods which are used for other digital data.