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Top Document: comp.compression Frequently Asked Questions (part 2/3) Previous Document: [75] Introduction to JPEG Next Document: [77] Introduction to Fractal compression (long) See reader questions & answers on this topic! - Help others by sharing your knowledge Some vector quantization software for data analysis that is available in the ftp://cochlea.hut.fi/pub/ directory. One package is lvq_pak and one is som_pak (som_pak generates Kohonen maps of data using lvq to cluster it). A VQ-based codec that is based on the Predictive Residual Vector Quantization is in ftp://mozart.eng.buffalo.edu/pub/prvq_codec/ VQ software is also available in ftp://isdl.ee.washington.edu/pub/VQ/ For a book on Vector Quantization, see the reference (Gersho and Gray) given in item 7 of this FAQ. For a review article: N. M. Nasrabadi and R. A. King, "Image Coding Using Vector Quantization: A review", IEEE Trans. on Communications, vol. COM-36, pp. 957-971, Aug. 1988. A short introduction to Vector Quantization, written by Alex Zatsman <alex.zatsman@analog.com>: In Scalar Quantization one represents the values by fixed subset of representative values. For examples, if you have 16 bit values and send only 8 most signifcant bits, you get an approximation of the original data at the expense of precision. In this case the fixed subset is all the 16-bit numbers divisable by 256, i.e 0, 256, 512,... In Vector Quantization you represent not individual values but (usually small) arrays of them. A typical example is a color map: a color picture can be represented by a 2D array of triplets (RGB values). In most pictures those triplets do not cover the whole RGB space but tend to concetrate in certain areas. For example, the picture of a forest will typically have a lot of green. One can select a relatively small subset (typically 256 elements) of representative colors, i.e RGB triplets, and then approximate each triplet by the representative of that small set. In case of 256 one can use 1 byte instead of 3 for each pixel. One can do the same for any large data sets, especialy when consecutive points are correlated in some way. CELP speech compression algorithms use those subsets "codebooks" and use them to quantize exciation vectors for linear prediction -- hence the name CELP which stands for Codebook Excited Linear Prediction. (See item 26 in part 1 of this FAQ for more information about CELP.) Note that Vector Quantization, just like Scalar Quantization, is a lossy compression. User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: comp.compression Frequently Asked Questions (part 2/3) Previous Document: [75] Introduction to JPEG Next Document: [77] Introduction to Fractal compression (long) Part1 - Part2 - Part3 - Single Page [ Usenet FAQs | Web FAQs | Documents | RFC Index ] Send corrections/additions to the FAQ Maintainer: jloup@gzip.OmitThis.org
Last Update March 27 2014 @ 02:11 PM
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comp.compression Frequently Asked Questions (part 2/3)
Section - [76] What is Vector Quantization?
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comp.compression Frequently Asked Questions (part 2/3)
Section - [76] What is Vector Quantization?
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