Below we present the most commonly asked questions about the SPIHT programs. Questions about the method and image compression in general are answered in the technical discussion page.
If you also have a question, or are not satisfied with an answer, send an e-mail to one address listed in the home page.
A. Actually, the programs were written and tested in a PC! So, no special version is needed. We have 6 programs using the same files, and what is missing is the #define directives to inform the compiler what program to make (this is presently done in the make (.mk) and script files).
In a DOS/Windows compiler you need to add the words shown below in the DEFINE menu of the compilers. The project files are codetree.c, general.c, image_bw.c plus the file shown below.
Program - Define - Project file codetree - ENCODER - aritcode.c decdtree - DECODER - aritcode.c fastcode - ENCODER;BINCODE - bincode.c fastdecd - DECODER;BINCODE - bincode.c progcode - ENCODER;LOSSLESS - aritcode.c progdecd - DECODER;LOSSLESS - aritcode.c
Q. What is the meaning of the error message below?
Error: < Image_BW > dimension is too small or negative
A. Consider the following practical problems
Those problems are irrelevant when the images are large enough, and so we may not have used in our programs the best method to deal with them. To avoid unfair comparisons we just made the programs refuse to code very small images (the minimum size depends on the program version). See below more about the same subject
A. The smoothing factor changes the scaling of the wavelet (subband) transform coefficients. This way there is a larger number of highpass coefficients set to zero, leaving more bits to represent the lowpass components. The name "smoothing" is used because the process does that, and is somewhat equivalent to a nonlinear lowpass filtering.
Note that it is not similar to the linear lowpass filtering used by other programs for entropy reduction.
A. Try this link to get the basic rules (at the end) and see below why you should follow them.
Some compression methods exploit the equality of pixels values and others exploit their similarity. It sounds like the same thing, but the difference in practice can be enormous.
Graphical images or cartoons, which have pixels in large areas with exactly the same value, compress well with general-purpose methods (like the Lempel-Ziv). On the other hand, for natural images the adjacent pixels tend to have similar, but rarely equal gray or color values. Some shading variations are hard to see, until you remove them using gross quantization, and producing really awful images. The general purpose methods fail miserably to detect similarity, and have little use for natural images.
Thus, you must use methods like SPIHT to compress natural images. They work well in a really wide range of pictures, in fact wider than we can define. (What is the resemblance between pictures of a wedding party and of Mount Fuji and a chest X-ray?)
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