huffman compression in java working projects free download
#1

hello sir, i want to make a project for compression file.
your project will be helpful for me.
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#2

Huffman coding is a lossless data compression algorithm. The idea is to assign the variable-legth codes to the input characters, the lengths of the assigned codes are based on the frequencies of the corresponding characters. The most frequent character gets the smaller code and the less frequent character gets the larger code.

The variable length codes assigned to the input characters are prefix codes, meaning that the codes (bit sequences) are assigned in such a way that the code assigned to a character is not a code prefix assigned to any other character. This is how Huffman Coding ensures that there is no ambiguity when decoding the generated bit stream.

Let's understand the prefixes with an example counter. There are four characters a, b, c and d, and their corresponding variable length codes are 00, 01, 0 and 1. This coding leads to ambiguity because the code assigned to c is the prefix of the codes assigned to a and b. If the compressed bitstream is 0001, the uncompressed output may be "cccd" or "ccb" or "acd" or "ab".

See this for Huffman Coding applications.
There are mainly two main parts in Huffman Encoding
1) Build a Huffman Tree input character.
2) Cross the Huffman Tree and assign codes to the characters.
Steps to Build Huffman Tree
The input is an array of unique characters along with their frequency of occurrences and the output is Huffman Tree.
1. Create a leaf node for each unique character and build a min heap of all leaf nodes (Min Heap is used as a priority queue. The value of the frequency field is used to compare two nodes in min stack. At first, The less frequent character is at the root)
2. Extract two nodes with the min.
3. Create a new internal node with a frequency equal to the sum of the two frequencies of the nodes. Make the first node extracted as its left child and the other node extracted as its right child. Add this node to the min.
4. Repeat steps 2 and 3 until the heap contains only one node. The remaining node is the root node and the tree is complete.
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