The.Algorithm.Design.Manual.Springer-Verlag.1998

Text Compression
have been corrupted. However, fidelity is not such an issue in image or video compression, where
the presence of small artifacts will be imperceptible to the viewer. Significantly greater
compression ratios can be obtained using lossy compression, which is why all image/video/audio
compression algorithms take advantage of this freedom.
● Can I simplify my data before I compress it? - The most effective way to free up space on a disk is
to delete files you don't need. Likewise, any preprocessing you can do to a file to reduce its
information content before compression will pay off later in better performance. For example, is it
possible to eliminate extra blank spaces or lines from the file? Can the document be converted
entirely to uppercase characters or have formatting information removed?
● Does it matter whether the algorithm is patented? - One concern is that many data compression
algorithms are patented, in particular the LZW variation of the Lempel-Ziv algorithm discussed
below. Further, Unisys, the owner of the patent, makes periodic attempts to collect. My personal
(although not legal) recommendation is to ignore them, unless you are in the business of selling
text compression software. If this makes you uncomfortable, note that there are other variations
on the Lempel-Ziv algorithm that are not under patent protection and perform about as well. See
the notes and implementations below.
● How do I compress image data - Run-length coding is the simplest lossless compression
algorithm for image data, where we replace runs of identical pixel values with one instance of the
pixel and an integer giving the length of the run. This works well on binary images with large
regions of similar pixels (like scanned text) and terribly on images with many quantization levels
and a little noise. It can also be applied to text with many fields that have been padded by blanks.
Issues like how many bits to allocate to the count field and the traversal order converting the twodimensional
image to a stream of pixels can have a surprisingly large impact on the compression
ratio.
For serious image and video compression applications, I recommend that you use a lossy coding
method and not fool around with implementing it yourself. JPEG is the standard highperformance
image compression method, while MPEG is designed to exploit the frame-to-frame
coherence of video. Encoders and decoders for both are provided in the implementation section.
● Must compression and decompression both run in real time? - For many applications, fast
decompression is more important than fast compression, and algorithms such as JPEG exist to
take advantage of this. While compressing video for a CD-ROM, the compression will be done
only once, while decompression will be necessary anytime anyone plays it. In contrast, operating
systems that increase the effective capacity of disks by automatically compressing each file will
need a symmetric algorithm with fast compression times as well.
Although there are literally dozens of text compression algorithms available, they are characterized by
two basic approaches. In static algorithms, such as Huffman codes, a single coding table is built by
analyzing the entire document. In adaptive algorithms, such as Lempel-Ziv, a coding table is built on the
fly and adapts to the local character distribution of the document. An adaptive algorithm will likely prove
to be the correct answer:
● Huffman codes - Huffman codes work by replacing each alphabet symbol by a variable-length
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