spiht algoritm based image compression

matlab program for developing spiht algorithm must be much better than jpeg version in all cases.

The SPIHT method is not a simple extension of traditional methods for image compression, and represents an important advance in the field. The method deserves special attention because it provides the following:
Highest Image Quality
Progressive image transmission
Fully embedded coded file
Simple quantization algorithm
Fast coding/decoding
Completely adaptive
Lossless compression
Exact bit rate coding
Error protection

Encoding/Decoding Speed
The SPIHT process represents a very effective form of entropy-coding. This is shown by the demo programs using two forms of coding: binary-uncoded (extremely simple) and context-based adaptive arithmetic coded (sophisticated). Surprisingly, the difference in compression is small, showing that it is not necessary to use slow methods (and also pay royalties for them!). A fast version using Huffman codes was also successfully tested, but it is not publicly available.
A straightforward consequence of the compression simplicity is the greater coding/decoding speed. The SPIHT algorithm is nearly symmetric, i.e., the time to encode is nearly equal to the time to decode. (Complex compression algorithms tend to have encoding times much larger than the decoding times.)
Some of our demo programs use floating-point operations extensively, and can be slower in some CPUs (floating points are better when people want to test you programs with strange 16 bpp images). However, this problem can be easily solved: try the lossless version to see an example. Similarly, the use for progressive transmission requires a somewhat more complex and slower algorithm. Some shortcuts can be used if progressive transmission is not necessary.
When measuring speed please remember that these demo programs were written for academic studies only, and were not fully optimized as are the commercial versions.

Applications
SPIHT exploits properties that are present in a wide variety of images. It had been successfully tested in natural (portraits, landscape, weddings, etc.) and medical (X-ray, CT, etc) images. Furthermore, its embedded coding process proved to be effective in a broad range of reconstruction qualities. For instance, it can code fair-quality portraits and high-quality medical images equally well (as compared with other methods in the same conditions).
SPIHT has also been tested for some less usual purposes, like the compression of elevation maps, scientific data, and others.