Quantization (image processing): Difference between revisions

Revision as of 18:13, 30 May 2007

Revision as of 14:30, 8 June 2007

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'''Quantization''', involved in [[image processing]]. Quantization, is a [[lossy compression]] technique achieved by compressing a range of values to a single quantum value. By reducing the number of discrete symbols in a given stream, the stream becomes more compressible. For example, seeking to reduce the number of colors required to represent an [[image]]. Another widely used example — [[Discrete cosine transform|DCT]] data quantization in [[JPEG]] and [[Discrete wavelet transform|DWT]] data quantization in [[JPEG 2000]].

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== Color quantization ==

{{mainarticle|Color quantization}}

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The human eye is fairly good at seeing small differences in [[brightness]] over a relatively large area, but not so good at distinguishing the exact strength of a high frequency brightness variation. This fact allows one to get away with greatly reducing the amount of information in the high frequency components. This is done by simply dividing each component in the frequency ___domain by a constant for that component, and then rounding to the nearest integer. This is the main lossy operation in the whole process. As a result of this, it is typically the case that many of the higher frequency components are rounded to zero, and many of the rest become small positive or negative numbers.

=== Quantization matrices ===

This is an example DCT coefficient matrix: <!--NOTE: this matrix was generated using random numbers and the other two matricies. It may not actually work well with an iDCT. -->