download matlab source code for spiht algorithm

[sanaja]

This WW page presents the powerful wavelet-based image compression method called Set Partitioning in Hierarchical Trees (SPIHT). This award-winning method has received worldwide acclaim and attention since its introduction via this web site in 1995. Thousands of people, researchers and practioners alike, have now tested and used SPIHT. It has become the benchmark state-of-the-art algorithm for image compression.

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:

good image quality, high PSNR, especially for color images;
it is optimized for progressive image transmission;
produces a fully embedded coded file;
simple quantization algorithm;
fast coding/decoding (nearly symmetric);
has wide applications, completely adaptive;
can be used for lossless compression.
can code to exact bit rate or distortion;
efficient combination with error protection.
Each of these properties is discussed below. Note that different compression methods were developed specifically to achieve at least one of those objectives. What makes SPIHT really outstanding is that it yields all those qualities simultaneously. So, if in the future you find one method that claims to be superior to SPIHT in one evaluation parameter (like PSNR), remember to see who wins in the remaining criteria.

Image Quality

Extensive research has shown that the images obtained with wavelet-based methods yield very good visual quality. At first it was shown that even simple coding methods produced good results when combined with wavelets. SPIHT belongs to the next generation of wavelet encoders, employing more sophisticated coding. In fact, SPIHT exploits the properties of the wavelet-transformed images to increase its efficiency.

Many researchers now believe that encoders that use wavelets are superior to those that use DCT or fractals. We will not discuss the matter of taste in the evaluation of low quality images, but we do want to say that SPIHT wins in the test of finding the minimum rate required to obtain a reproduction indistinguishable from the original. The SPIHT advantage is even more pronounced in encoding color images, because the bits are allocated automatically for local optimality among the color components, unlike other algorithms that encode the color components separately based on global statistics of the individual components. You will be amazed to see that visually lossless color compression is obtained with some images at compression ratios from 100-200:1.

If, after what we said, you are still not certain that you should believe us (because in the past you heard claims like that and then were deeply disappointed), we understand you point of view.

There are three things you can do to convince yourself

Take a look at some of our images.
Visit John Kominek's Waterloo BragZone, where you will find an independent comparative evaluation of different image compression methods. Several methods were tested, and SPIHT proved to be outstanding. (Note: our programs are called SAPA & SAPB there.) In the Waterloo BragZone you will find plenty of images, graphs, etc. If you note that some method or program is missing, consider that maybe their authors refused to have it compared!
Use our demo programs to test SPIHT in your own images.
While using the programs please recall that:

SPIHT is a coding method. So, if you find a reconstruction artifact remember that it may be caused by the particular wavelet transform used by the encoder, and not by the coding process. The choice of the best transform for different image types is still open.
The presently available versions of SPIHT were not designed for synthetic graphical images, so read below what we have to say about them.
The lossy compression programs have a "smoothing factor" that is different from the smoothing factor used by other programs (see FAQ).

[rijokuruvila]

Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. There are several different ways in which image files can be compressed. For Internet use, the two most common compressed graphic image formats are the JPEG format and the GIF format. The JPEG method is more often used for photographs, while the GIF method is commonly used for line art and other images in which geometric shapes are relatively simple. Other techniques for image compression include the use of fractals and wavelets. These methods have not gained widespread acceptance for use on the Internet as of this writing. However, both methods offer promise because they offer higher compression ratios than the JPEG or GIF methods for some types of images. Another new method that may in time replace the GIF format is the PNG format. Compressing an image is significantly different than compressing raw binary data. Of course, general-purpose compression programs can be used to compress images, but the result is less than optimal. This is because images have certain statistical properties, which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image can be sacrificed for the sake of saving a little more bandwidth or storage space. This also means that lossy compression techniques can be used in this area. A text file or program can be compressed without the introduction of errors, but only up to a certain extent. This is called lossless compression. Beyond this point, errors are introduced. In text and program files, it is crucial that compression be lossless because a single error can seriously damage the meaning of a text file, or cause a program not to run. In image compression, a small loss in quality is usually not noticeable. There is no "critical point" up to which compression works perfectly, but beyond which it becomes impossible. When there is some tolerance for loss, the compression factor can be greater than it can when there is no loss tolerance. For this reason, graphic images can be compressed more than text files or programs. -

[[email protected]]

Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. There are several different ways in which image files can be compressed. For Internet use, the two most common compressed graphic image formats are the JPEG format and the GIF format. The JPEG method is more often used for photographs, while the GIF method is commonly used for line art and other images in which geometric shapes are relatively simple. Other techniques for image compression include the use of fractals and wavelets. These methods have not gained widespread acceptance for use on the Internet as of this writing. However, both methods offer promise because they offer higher compression ratios than the JPEG or GIF methods for some types of images. Another new method that may in time replace the GIF format is the PNG format. Compressing an image is significantly different than compressing raw binary data. Of course, general-purpose compression programs can be used to compress images, but the result is less than optimal. This is because images have certain statistical properties, which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image can be sacrificed for the sake of saving a little more bandwidth or storage space. This also means that lossy compression techniques can be used in this area. A text file or program can be compressed without the introduction of errors, but only up to a certain extent. This is called lossless compression. Beyond this point, errors are introduced. In text and program files, it is crucial that compression be lossless because a single error can seriously damage the meaning of a text file, or cause a program not to run. In image compression, a small loss in quality is usually not noticeable. There is no "critical point" up to which compression works perfectly, but beyond which it becomes impossible. When there is some tolerance for loss, the compression factor can be greater than it can when there is no loss tolerance. For this reason, graphic images can be compressed more than text files or programs.

[deepthy]

These programs have been compiled and used by a lot of users, in many computers, and in all continents except Antarctica. The supplied executables are run from the UNIX or DOS command line prompt. Don't get discouraged if something goes wrong. Look for command syntax or path specification errors.

Still Images

The programs immediately below implement compression and decompression of monochrome or color still images stored in headerless format, 1 or 2 bytes per pel. The *.z and *.gz files have been compressed with gzip and must be decompressed with gunzip. The archive files with .tar extensions (after decompression) are exploded with tar xvf.

The files with .zip extensions are Windows/DOS files and can be exploded using WinZip or PKUNZIP.
Source Code

!NEW SPIHT in MATLAB Programming Language

We have made publicly available a demonstration version of SPIHT image compression programmed in MATLAB language. Please read the document mSpiht_Readme.pdf for program information and license terms that incorporate the GNU Public License. Download MATLAB-SPIHT.

You may obtain independently developed source code for the SPIHT monochrome image codec free of charge under the conditions of the General GNU License in the program library QccPack as an optional module named QccPackSPIHT.

The following programs do reversible (lossless) image compression. They do not use SPIHT.
lossless.tar.gz and lossless.zip
Compressed archive file for reversible (truly perfect) compression/decompression programs. These programs are inherently progressive in resolution, but currently the decoder outputs only the final full size image. See *.doc (embedded) file for compiling & running instructions.

Executable Program Code

The compressed archive file packages below contain the SPIHT image compression/decompression programs (codecs). There are four codecs: one for color images and three for monochrome images. Among the monochrome codecs, two are for progressive lossy to quasi-perfect reconstruction, with different coding speeds. Finally, there is also a codec for progressive reconstruction up to truly perfect reconstruction. File SPIHT.doc has the instructions for running the programs and more details about the different codecs.

[anish m jose]

Hi dear I need matlab code for spiht algorithm original and modified version as well. please provide me i will be very thankful to you.

[bhargav]

Hii..i am in need of matlab code for image compression using spiht algorithm..can u provide tat code..