  An algorithm is proposed for the segmentation of image into multiple levels
using mean and standard deviation in the wavelet domain. The procedure provides
for variable size segmentation with bigger block size around the mean, and
having smaller blocks at the ends of histogram plot of each horizontal,
vertical and diagonal components, while for the approximation component it
provides for finer block size around the mean, and larger blocks at the ends of
histogram plot coefficients. It is found that the proposed algorithm has
significantly less time complexity, achieves superior PSNR and Structural
Similarity Measurement Index as compared to similar space domain algorithms[1].
In the process it highlights finer image structures not perceptible in the
original image. It is worth emphasizing that after the segmentation only 16 (at
threshold level 3) wavelet coefficients captures the significant variation of
image.
