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Mentally, we are used to a type of adaptive dictionary when performing acronym replacements in technical literature. The standard way to use this adaptive dictionary is to spell out the acronym, then put its abbreviated substitution in parentheses. So the first time I mention the Massachusetts Institute of Technology (MIT), I define both the dictionary string and its substitution. From then on, referring to MIT in the text should automatically invoke a mental substitution.
Until 1980, most general-compression schemes used statistical modeling. But in 1977 and 1978, Jacob Ziv and Abraham Lempel described a pair of compression methods using an adaptive dictionary. These two algorithms sparked a flood of new techniques that used dictionary-based methods to achieve impressive new compression ratios.
The first compression algorithm described by Ziv and Lempel is commonly referred to as LZ77. It is relatively simple. The dictionary consists of all the strings in a window into the previously read input stream. A file-compression program, for example, could use a 4K-byte window as a dictionary. While new groups of symbols are being read in, the algorithm looks for matches with strings found in the previous 4K bytes of data already read in. Any matches are encoded as pointers sent to the output stream.
LZ77 and its variants make attractive compression algorithms. Maintaining the model is simple; encoding the output is simple; and programs that work very quickly can be written using LZ77. Popular programs such as PKZIP and LHarc use variants of the LZ77 algorithm, and they have proven very popular.
The LZ78 program takes a different approach to building and maintaining the dictionary. Instead of having a limited-size window into the preceding text, LZ78 builds its dictionary out of all of the previously seen symbols in the input text. But instead of having carte blanche access to all the symbol strings in the preceding text, a dictionary of strings is built a single character at a time. The first time the string Mark is seen, for example, the string Ma is added to the dictionary. The next time, Mar is added. If Mark is seen again, it is added to the dictionary.
This incremental procedure works very well at isolating frequently used strings and adding them to the table. Unlike LZ77 methods, strings in LZ78 can be extremely long, which allows for high-compression ratios. LZ78 was the first of the two Ziv-Lempel algorithms to achieve popular success, due to the LZW adaptation by Terry Welch, which forms the core of the UNIX compress program.
Until recently, lossy compression has been primarily performed on special-purpose hardware. The advent of inexpensive Digital Signal Processor (DSP) chips began lossy compressions move off the circuit board and onto the desktop. CPU prices have now dropped to where it is becoming practical to perform lossy compression on general-purpose desktop PCs.
Lossy compression is fundamentally different from lossless compression in one respect: it accepts a slight loss of data to facilitate compression. Lossy compression is generally done on analog data stored digitally, with the primary applications being graphics and sound files.
This type of compression frequently makes two passes. A first pass over the data performs a high-level, signal-processing function. This frequently consists of transforming the data into the frequency domain, using algorithms similar to the well-known Fast Fourier Transform (FFT). Once the data has been transformed, it is smoothed, rounding off high and low points. Loss of signal occurs here. Finally, the frequency points are compressed using conventional lossless techniques.
The smoothing function that operates on the frequency-domain data generally has a quality factor built into it that determines just how much smoothing occurs. The more the data is massaged, the greater the signal lossand more compression will occur.
In the small systems world, a tremendous amount of work is being done on graphical image compression, both for still and moving pictures. The International Standards Organization (ISO) and the Consultive Committee for International Telegraph and Telephone (CCITT) have banded together to form two committees: The Joint Photographic Experts Group (JPEG) and the Moving Pictures Expert Group (MPEG). The JPEG committee has published its compression standard, and many vendors are now shipping hardware and software that are JPEG compliant. The MPEG committee completed an intial moving picture compression standard, and is finalizing a second, MPEG-II.
The JPEG standard uses the Discrete Cosine Transform (DCT) algorithm to convert a graphics image to the frequency domain. The DCT algorithm has been used for graphics transforms for many years, so efficient implementations are readily available. JPEG specifies a quality factor of 0 to 100, and it lets the compressor determine what factor to select.
Using the JPEG algorithm on images can result in dramatic compression ratios. With little or no degradation, compression ratios of 9095 percent are routine. Accepting minor degradation achieves ratios as high as 9899 percent.
Software implementations of the JPEG and MPEG algorithms are still struggling to achieve real-time performance. Most multimedia development software that uses this type of compression still depends on the use of a coprocessor board to make the compression take place in a reasonable amount of time. We are probably only a few years away from software-only real-time compression capabilities.
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