It is one of the most successful Encoding Algorithms. Encoding means to convert the text in some other format. We generally perform encoding for reducing the size of the file. Suppose a text file is available then we can convert the text file in some other format which might be taking less space i. Converting in the form of binary string would be beneficial because to store a binary 0 or 1 we need only one bit but to store a character we need 8 bits.
Loseless Compression reduces a file's size with no loss of quality. For loseless compression, we rewrite the data of the original file in a more efficient way mostly in the form of binay string.
For compression, a table representing the frequency of each character in a file is being built. Using table an optimal way for representing each character as a binary string is being determined. Both the encoding techniques would definetly reduce the size of the file but Variable-Length encoding is better than Fixed-Length encoding. Let's understand this with an example. Suppose there is a file for which we have created a table representing the frequency of each character as follows:.
Fixed-Length Encoding means assigning each character binary codes of fixed length. Since there are 6 characters so we need 3 bits to store each character uniquely. So,total bits required to store the whole file is 3. Since in this method each character is being assigned variable length binary codes so what we try to do here is to assign frequent characters short code words and unfrequent characters long code words. Consider the scheme:. In this case,total bits required to store the whole file is One can easily see the difference between the spaces required to store a file using Fixed-Length Encoding and Variable-Length Encoding.
So,we can conclude that Variable-Length Encoding is much better. After having enough insight about encoding let's now learn about Huffman Encoding. So 2,3,2,3,2 becomes — Which fits in 2 bytes.
If you want to get really crazy, you could do what DEFLATE does, and make another huffman table of the lengths of these codes, and store its code lengths beforehand. Then output the codes for the message. You then have a long series of bits that you can divide up into characters for output. The tree is generally created from a frequency table of the bytes. So store that table, or just the bytes themselves sorted by frequency, and re-create the tree on the fly.
This page describes the way a tree is built from the frequency table. As a bonus, it also saves this answer from being deleted by mentioning a way to save out the tree:. The easiest way to output the huffman tree itself is to, starting at the root, dump first the left hand side then the right hand side. For each node you output a 0, for each leaf you output a 1 followed by N bits representing the value.
So just keep the vector of uncompressed values [A B C D E F] ordered by tree depth, use relative indexes instead to this separate vector. Recommended Articles. Minimize operations to convert A to B by adding any odd integer or subtracting any even integer. Minimize Array elements to be reduced to make subsequences sum 1 to Array max possible.
Article Contributed By :. Easy Normal Medium Hard Expert. Writing code in comment? Please use ide. Load Comments. Where is your code? Which programming language? I am coding in java. Can you plz give me some idea that how can I save tree in file so, it can be again used for decoding.
Add a comment. Active Oldest Votes. Thanks for your help, but i am not facing problem in building tree and assigning codes to characters. I am not understanding that how can we save tree in a file so that same tree can be used to decode the code? But that's exactly what the code I gave you does! The sequence of bits precisely encodes the shape of the tree.
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