Embedded zerotrees of wavelet transforms (EZW) is a lossy image compression algorithm.
This occurs because "real world" images tend to contain mostly low frequency information (highly correlated).
However where high frequency information does occur (such as edges in the image) this is particularly important in terms of human perception of the image quality, and thus must be represented accurately in any high quality coding scheme.
In zerotree based image compression scheme such as EZW and SPIHT, the intent is to use the statistical properties of the trees in order to efficiently code the locations of the significant coefficients.
Embedded zerotree wavelet algorithm (EZW) as developed by J. Shapiro in 1993, enables scalable image transmission and decoding.
It is based on four key concepts: first, it should be a discrete wavelet transform or hierarchical subband decomposition; second, it should predict the absence of significant information when exploring the self-similarity inherent in images; third, it has entropy-coded successive-approximation quantization, and fourth, it is enabled to achieve universal lossless data compression via adaptive arithmetic coding.
Besides, the EZW algorithm also contains the following features: (1) A discrete wavelet transform which can use a compact multiresolution representation in the image.
(4) A prioritization protocol which the importance is determined by the precision, magnitude, scale, and spatial location of the wavelet coefficients in order.
In this method, it will visit the significant coefficients according to the magnitude and raster order within subbands.