Data Compression (2000)
By Ginger Rosenkrans
Introduction
Compression
techniques reduce the amount of memory needed for storing still and video images
and the bandwidth for transporting them (Dipert, 1998; Saha, 2000; Wu &
Irwin, 1998). Even the smallest or simplest picture on a Web page is compressed
before it is placed on the page, and then it is decompressed by the user’s
computer. Compression is a process where algorithms reduce the number of bytes
needed to encode data (England & Finney, 1999; Tannenbaum, 1998). Video in
multimedia is digital; therefore, it has to be compressed due to the high data
rate used by raw video, storage devices that are too slow, and networks that
have too narrow bandwidth to rapidly transmit the data that is needed to deliver
the multimedia presentation in real time (England & Finney, 1999; Tannenbaum,
1998). There are at least two kinds of compression algorithms: lossy and
lossless (Tannenbaum, 1998; Web Reference, 2000). Additionally, video
compression is commonly known as bit rate reduction. JPEG and MPEG are two
standards applied to video compression, and GIF is an image format widely used
on the Web (Dipert, 1998; England & Finney, 1999; Olsen, 1997; Web
Reference, 2000; Wu & Irwin, 1998).
Lossy
and Lossless Compression
Lossy compression
creates smaller files by discarding some information about the original image (Tannenbaum,
1998; Web Reference, 2000). Lossy algorithms remove details and color
alterations that are deemed too small for the brain to detect. They do not
significantly reduce the effect of the presentation; therefore, some distortion
is acceptable because it may be imperceptible or may not distract the brain (Tannenbaum,
1998). Additionally, lossy compression algorithms are not perfectly reversible.
When a compressed file is decompressed, the data is not bit-by-bit identical
with the original data. It does not significantly modify the perception of the
data from what was originally intended. Motion and still image files can be
compressed using lossy algorithms (Tannenbaum, 1998; Web Reference, 2000).
A lossless
compression algorithm does not discard any information about the original file (Saha,
2000; Tannenbaum, 1998; Web Reference, 2000). The data retrieved is identical to
the original data. Saha (2000) asserts that in lossless compression, the
reconstructed image after compression is "numerically identical to the
original image on a pixel-by-pixel basis." Lossless compression is done by
a recording of the original data using an efficient code that takes less
storage. One type of lossless compression algorithm is run-length coding, which
"replaces the long sequence of identical big patterns with one instance of
the pattern plus a number specifying how many times the pattern is to be
repeated" (Tannenbaum, 1998, p. 335). If a multimedia program needs to be
compressed for transmission or storage, a lossless algorithm is used.
JPEG,
GIF, and MPEG
JPEG, GIF, and MPEG
are common compression algorithms (Dipert, 1998; England & Finney, 1999;
Tannenbaum, 1998; Web Reference, 2000; Wu & Irwin, 1998). JPEG, which is the
common name for the raster format defined by the Joint Photographic Experts
Groups, is one of the best methods to compress photographic images (Coffee,
1997; Dipert, 1998; Saha, 2000; Siegel, 1997). GIF, which is the acronym for
Graphics Interchange Format, is one of the oldest graphic formats on the Web.
MPEG, which is the name for Motion Picture Experts Group, is a standard for
distributed system video (Olsen, 1997; Tannenbaum, 1998).
Lossy JPEG which is
a lossy compression algorithm, is designed for use with continuous tone images
like photographs or natural artwork (Dibert, 1998; Olsen, 1997; Saha, 2000;
Siegel, 1997; Web Reference, 2000; Wu & Irwin, 1998). JPEG separates the
brightness information from the color hues to reduce file size and converts the
spatial image representation into a frequency map (Siegel, 1997). The greater
the compression, the lossier the file will be. Siegel (1997) recommends starting
with the lowest-quality JPEG setting, discarding the most information, and
reducing file size as much as possible.
The GIF format uses
a lossless compression algorithm (Siegel, 1997; Web Reference, 2000). The GIF
format uses a variant of the Lempel-Ziv-Welch (LZW) compression (Siegel, 1997;
Web Reference, 2000). LZW, a lossless compression algorithm, is a
repeated-string compressor and uses a translation table to represent linear
sequences of data in the uncompressed input stream (Siegel, 1997; Web Reference,
2000). When a sequence is first encountered, a code is added to the translation
table and subsequent matching sequences are represented by the code. Generally,
GIF compression is used for anything that is not photographic (e.g., line art,
type). Additionally, the average compression ration for GIF images is 4:1. GIFs
can support up to 8 bits per pixel, which is a maximum of 256 colors (Web
Reference, 2000).
MPEG compression
includes the compression of moving picture images and the accompanying audio
(England & Finney, 1999; Olsen, 1997; Tannenbaum, 1998). MPEG compression
for audio is accomplished by a subband coding process, and compression for
images is accomplished by encoding only the changes in images that occur from
frame to frame (Coffee, 1997; Olsen, 1997; Tannenbaum, 1998)
References
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Dipert, B.(1998,
June 18). Compression puts images on a diet. EDN, 43 (13), 71.
England, E., &
Finney, A. (1999). Managing Multimedia (2nd ed.). Harlow,
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Melamed, B. (1997).
Modeling compressed full-motion video. Proceedings of the 1997 Winter
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Olsen, G. (1997). Multimedia
Design. Cincinnati, OH: F&W Publications.
Saha, S. (2000,
Spring). Image compression—from DCT to wavelets: a review. Crossroads,
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Siegel, D. (1997). Creating
Killer Web Sites. Indianapolis, IN: Hayden Books.
Tannenbaum, R. S.
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Web Reference.
(2000, February 24). Compression: Optimizing Web Graphics. Available: http://www.webreference.com/dev/graphics/compress.htm
Accessed February 25, 2000.
Wu, C. & Irwin
J. D. (1998). Emerging Multimedia Computer Communication Technologies,
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