Algorithm is easy to implement
Produce a lossless compression of images
huffman has a better compression rate.
Huffman Code is greedy when it locally (remember Greedy algorithms chooses the best solution at that time) chooses and merges two of the smallest nodes (nodes are weighted after occurrence/frequency. Those which occur most frequent has the largest values, and those with that occur least has the lowest values) at a time, until there is no more nodes left over and a binary tree is built, with left edges marked as 0's, and the right edges marked as 1's.
No it is not
CODING Implementation
Disadvantages: you would have to learn a powerful coding language, which might take a lot of time. you would have to choose out of the popular coding languages, e.g. Java, C++, C#, C, Python, etc, etc. It would take a LONG time to write the actual program. Advantages: you could profit on it, depending on how good or bad your program is. you would be praised on your knowledge of coding languages.
1.the compression ratio is higher compared to huffman coding. 2.efficiency is greater comparatively. 3.Redundancy is much reduced.
Kodam
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.
lossless
golf
Types of testing is broadly classified as Black box testing and white box testing
Codes are uniqueCodes are short
not alot
No... Kim Huffman is Canadian and Felicity Huffman is American.
it help in reducing ambiguity.
1) In Shannon-Fano coding, we cannot be sure about the codes generated. There may be two different codes for the same symbol depending on the way we build our tree. 2) Also, here we have no unique code i.e a code might be a prefix for another code. So in case of errors or loss during data transmission, we have to start from the beginning. 3) Shannon-Fano coding does not guarantee optimal codes. Hence, Shannon-Fano coding is not very efficient Huffman coding is more efficient than Shannon-Fano coding.