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This is part 1, there is also a part 2 and a part 3. Run-length encoding RLE is probably the most important and fundamental string compression technique. Countless multimedia formats and protocols use one form or RLE compression or another. RLE is also deceptively simple. It represents repeated values as a counter and a character.
If that is all there was to RLE, then the wikipedia page on run-length encoding would be just fine. Yet, I think it needs help. There is no unique RLE format. How you use the RLE idea depends on your goals such as 1 maximize the compression rate 2 maximize the processing speed. Continue reading to part 2. Thanks Daniel for the beautifully concise explanation of RLE. Makes me wonder how it can seem complex in other explanations. I could try to guess what iterated hierarchical RLE with a fancy encoding means, but that is not a very interesting game.
As I told you this is something I stumbled upon when peeking at reverse engineering of a compression utility. Kevembuangga Let me put it differently. Could you implement the a scheme like the one you described? Your email address will not be published. For more help see http: Skip to content My home page My papers My software. Why do we use RLE? You can read and write RLE data in one pass, using almost no memory.
Given a vector V compressed with RLE, you can apply any scalar function f to its component in time O V where V is the compressed size of the vector. Some RLE formats have worse complexity bounds. Any downsides to RLE? Random access is slower. Sometimes, only sequential read from the beginning is possible. Updating an RLE-compressed array can be difficult. You need long runs of identical values.
Thus, if the compression rates are modest, it could actually take longer to process an RLE-compressed array. What is the RLE format? Here are some common variations: Instead of using a counter for each run of characters, you only add a counter after a value has been repeated twice. Thus, if many characters are not repeated, you will rarely use an unnecessary counter. You can use a single bit to decide whether a counter is used. Again, this may avoid many unnecessary counters if values are often not repeated.
Instead of a counter, you may store the location of the run in the array. The benefit of this approach is to allow random access in logarithmic time using binary search. However, it is also incompatible with some techniques to avoid unnecessary counters. So, it is a compression-speed trade-off. For even more speed, you can store both the location of the run and its length thus avoiding a subtraction.
To help vectorization , you can group the characters into blocks of k characters. Again, this is a compression-speed trade-off because there will be fewer runs to compress after grouping the characters into long blocks. Some References to my own work: Leave a Reply Cancel reply Your email address will not be published.
To create code blocks or other preformatted text, indent by four spaces: This will be displayed in a monospaced font. Markdown is turned off in code blocks: More database compression means more speed? Run-length encoding part 2.