# Huffman coding java

• Huffman Coding
• Huffman Compression Algorithm
• Algorithm Description
• Huffman Coding Compression Algorithm
• Huffman Coding | Greedy Algo-3
• Huffman Coding Algorithm
• ## Huffman Coding

The prefix rule states that no code is a prefix of another code. By code, we mean the bits used for a particular character. In the above example, 0 is the prefix of , which violates the prefix rule. If our codes satisfy the prefix rule, the decoding will be unambiguous and vice versa.

This time we assign codes that satisfy the prefix rule to characters 'a', 'b', 'c', and 'd'. Now we can uniquely decode back to our original string aabacdab. Huffman Coding The technique works by creating a binary tree of nodes. A node can be either a leaf node or an internal node. Initially, all nodes are leaf nodes, which contain the character itself, the weight frequency of appearance of the character.

Internal nodes contain character weight and links to two child nodes. As a common convention, bit 0 represents following the left child, and a bit 1 represents following the right child. A finished tree has n leaf nodes and n-1 internal nodes.

It is recommended that Huffman Tree should discard unused characters in the text to produce the most optimal code lengths. We will use a priority queue for building Huffman Tree, where the node with the lowest frequency has the highest priority. Following are the complete steps: 1. Create a leaf node for each character and add them to the priority queue. While there is more than one node in the queue: Remove the two nodes of the highest priority the lowest frequency from the queue.

Add the new node to the priority queue. The remaining node is the root node and the tree is complete. Consider some text consisting of only 'A', 'B', 'C', 'D', and 'E' characters, and their frequencies are 15, 7, 6, 6, 5, respectively. The following figures illustrate the steps followed by the algorithm: The path from the root to any leaf node stores the optimal prefix code also called Huffman code corresponding to the character associated with that leaf node.

## Huffman Compression Algorithm

In nerd circles, his algorithm is pretty well known. Often college computer science textbooks will refer to the algorithm as an example when teaching programming techniques. I wanted to keep the domain name in the family so I had to pay some domain squatter for the rights to it. It is a simple question, but one without an obvious solution.

In fact, my uncle took the challenge from his professor to get out of taking the final. This allows for possible characters because 2 to the 8th power is Unicode allocates 16 bits per character and it handles even non-Roman alphabets. The more bits you allow per character the more characters you can support in your alphabet.

It is simply easier for computers to handle characters when they all are the same size. But when you make every character the same size, it can waste space. In written text, all characters are not created equal. You could compound the savings by adjusting the size of every character and by more than 1 bit. Even before computers, Samuel Morse took this into account when assigning letters to his code.

Huffman Coding is a methodical way for determining how to best assign zeros and ones. It was one of the first algorithms for the computer age. By the way, Morse code is not really a binary code because it puts pauses between letters and words. This adjusting of the codes is called compression and sometimes the computational effort in compressing data for storage and later uncompressing it for use is worth the trouble.

The more space a text file takes up makes it slower to transmit from one computer to another. Other types of files, which have even more variability than the English language, compress even better than text.

Uncompressed sound. WAV and image. BMP files are usually at least ten times as big as their compressed equivalents. MP3 and. JPG respectively. Fax pages would take longer to transmit. You get the idea. All of these compressed formats take advantage of Huffman Coding. Again, the trick is to choose a short sequence of bits for representing common items letters, sounds, colors, whatever and a longer sequence for the items that are encountered less often.

When you average everything out, a message will require less space if you come up with good encoding dictionary. To avoid this ambiguity, we need a way of organizing the letters and their codes that prevents this. A good way of representing this information is something computer programmers call a binary tree.

Alexander Calder is an American artist who builds mobiles and really likes the colors red and black. One of his larger works hangs from the East building atrium at the National Gallery, but he had made several similar to it. The mobile hangs from a single point in the middle of a pole. It slowly sways as the air circulates in the room. Similarly, those lower poles have things hanging off of them too.

At the lowest levels, all the poles have weights on their ends. Programmers would look at this mobile and think of a binary tree, a common structure for storing program data.

This is because every mobile pole has exactly two ends. Let us build a mobile So how do we build that perfectly balanced mobile?

The first step of Huffman Coding is to count the frequency of all the letters in the text. Sticking with mobile analogy, we need to create a bunch of loose paddles, each one painted with a letter in the alphabet.

The weight of each paddle is proportional to the number of times that letter appears in the text. Every paddle has a loop for hanging. Now lets prepare some poles. In my imaginary world, poles weigh nothing. Now let us line up all the paddles then find the two lightest of them and connect them to opposite ends of a pole. Remember the pole itself weighs nothing.

The two lightest things in the room now may be an individual paddle or possibly a previously connected contraption. We are attaching the poles from the bottom up. So what do we do with this tree? In other words, the path from the top to the common letters will be the shortest binary sequence. The path from the top to the rare letters at the bottom will be much longer. To finish compressing the file, we need to go back and re-read the file. This implies that when we must have the tree around at the time we decompressing it.

Commonly this is accomplished by writing the tree structure at the beginning of the compressed file. This will make the compressed file a little bigger, but it is a necessary evil. You have to have the secret decoder ring before you can pass notes in class. Other ways of squeezing data Since my uncle devised his coding algorithm, other compression schemes have come into being.

When that is the case, it is occasionally worth the effort to adjust how the Huffman tree hangs while running through the file. One could slice the file into smaller sections and have different trees for each section. This is called Adaptive Huffman Coding. Sometimes it is not necessary to re-create the original source exactly. For example, with image files the human eye cannot detect every subtle pixel color difference.

The MP3 music format uses a similar technique for sound files.

## Algorithm Description

Repeat till the Priority Queue has only one node left. That node becomes the root of the Huffman Tree. Once the tree is built, to find the prefix code of each character we traverse the tree as: Starting at the top when you go left, append 0 to the prefix code string.

When you go right, append 1. Stop when you have reached the Leaf nodes. The string of 0 and 1s created till now is the prefix code of that particular Node in the tree. During decoding, we just need to print the character of each leaf traversed by the above prefix code in the Huffman tree. All Input characters are present only in the leaves of the Huffman tree. An example of a Huffman tree is given below: The string to be encoded needs the prefix codes for all the characters built in a bottom-up manner.

The internal node of any two Nodes should have a non-character set to it. The comparable interface is implemented. This would be used when comparing the values in the PriorityQueue in order to get the minimum value always.

## Huffman Coding Compression Algorithm

The code for the HuffmanCodeSolution. The more space a text file takes up makes it slower to transmit from one computer to another. Other types of files, which have even more variability than the English language, compress even better than text. Uncompressed sound.

### Huffman Coding | Greedy Algo-3

WAV and image. BMP files are usually at least ten times as big as their compressed equivalents. MP3 and. JPG respectively. Fax pages would take longer to transmit. You get the idea. All of these compressed formats take advantage of Huffman Coding. Again, the trick is to choose a short sequence of bits for representing common items letters, sounds, colors, whatever and a longer sequence for the items that are encountered less often.

When you average everything out, a message will require less space if you come up with good encoding dictionary. To avoid this ambiguity, we need a way of organizing the letters and their codes that prevents this.

A good way of representing this information is something computer programmers call a binary tree. Alexander Calder is an American artist who builds mobiles and really likes the colors red and black. One of his larger works hangs from the East building atrium at the National Gallery, but he had made several similar to it.

## Huffman Coding Algorithm

The mobile hangs from a single point in the middle of a pole. It slowly sways as the air circulates in the room. Similarly, those lower poles have things hanging off of them too. At the lowest levels, all the poles have weights on their ends.

Programmers would look at this mobile and think of a binary tree, a common structure for storing program data. Prepare for coding interviews at Amazon and other top product-based companies with our Amazon Test Series. Includes topic-wise practice questions on all important DSA topics along with 10 practice contests of 2 hours each. Designed by industry experts that will surely help you practice and sharpen your programming skills.

Wait no more, start your preparation today! 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 frequency field is used to compare two nodes in min heap. Initially, the least frequent character is at root Extract two nodes with the minimum frequency from the min heap.

Create a new internal node with a frequency equal to the sum of the two nodes frequencies. Make the first extracted node as its left child and the other extracted node as its right child.

Add this node to the min heap.

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• 14.08.2021 at 22:47

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