Wavelet compression is a form of data compression well suited for image compression (sometimes also video compression and audio compression). The goal is to store image data in as little space as possible in a file. Wavelet compression can be perfect, lossless data compression, or a certain loss of quality is accepted with lossy data compression.

Using a wavelet transform, the wavelet compression methods are adequate for representing transients, such as percussion sounds in audio, or high-frequency components in two-dimensional images, for example an image of stars on a night sky. This means that the transient elements of a data signal can be represented by a smaller amount of information than would be the case if some other transform, such as the more widespread discrete cosine transform, had been used.

Wavelet compression is not good for all kinds of data: transient signal characteristics mean good wavelet compression - smooth, periodic signals are better compressed by other methods. Data statistically similar to random noise may not be compressible by any means.

Method

First a wavelet transform is applied. This produces as many coefficients as there are pixels in the image (i.e.: there is no compression yet since it is only a transform). These coefficients can then be compressed more easily because the information is statistically concentrated in just a few coefficients. This principle is called transform coding. After that, the coefficients are quantized and the quantized values are entropy encoded and/or run length encoded.

Examples for wavelet compression:

Still images

JPEG 2000

ECW

MrSID

SPIHT

Embedded Zerotrees of Wavelet transforms / EZW

Progressive Graphics File

Video

Dirac

Pixlet

Snow

Tarkin

Rududu

Bink Video

Redcode

Motion Compensated Temporal Filtering (MCTF) can use wavelets both in time and in space