Step 2: Sort the probability list in descending order of probability.
The full procedure can be summarised as follows:
*Some implementations use a pseudo-symbol rather than the input stream length to determine when to stop decoding. The pseudo-symbol must be placed at the end of the probability list after step 2 and must be output prior to padding the output step 13.
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Huffman Coding is a method of shortening down messages sent from one computer to another so that it can be sent quicker.
huffman has a better compression rate.
1.the compression ratio is higher compared to huffman coding. 2.efficiency is greater comparatively. 3.Redundancy is much reduced.
lossless
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The time complexity of the Huffman coding algorithm is O(n log n), where n is the number of symbols in the input data.
Huffman coding can be implemented in Python by first creating a frequency table of characters in the input text. Then, a Huffman tree is built using a priority queue to assign binary codes to each character based on their frequency. Finally, the encoded text is generated by replacing characters with their corresponding Huffman codes.
Algorithm is easy to implement Produce a lossless compression of images
The Huffman coding algorithm is a method used for lossless data compression. It works by assigning shorter codes to more frequent symbols and longer codes to less frequent symbols, resulting in efficient encoding. The key principles include constructing a binary tree based on symbol frequencies, assigning codes based on tree traversal, and ensuring no code is a prefix of another. Applications of Huffman coding include file compression, data transmission, and image encoding.
Huffman coding has several disadvantages, including its reliance on the frequency of symbols, which can lead to inefficient encoding if the symbol distribution is not known in advance or changes frequently. Additionally, it requires the construction and storage of a binary tree, which can add complexity and overhead, especially for small data sets. The algorithm is also not suitable for real-time applications since it may require preprocessing time to build the tree before encoding. Lastly, Huffman coding does not handle dynamic data well, as it is typically static once the tree is constructed.
Types of testing is broadly classified as Black box testing and white box testing