how to encode a message into an image matlab

On the left-hand side, ‘e’ is attached to 4. Below the root node you can see the leaf nodes 16 and 20.

It is generated from the sentence “this is an example of a huffman tree” using Huffman algorithm. The model for Huffman tree is shown here in the figure. The main difference between the two methods is that Shannon–Fano constructs its codes from top to bottom (and the bits of each codeword are constructed from left to right), while Huffman constructs a code tree from the bottom up and the bits of each codeword are constructed from right to left. The Huffman coding method is somewhat similar to the Shannon–Fano method. A perfectly consistent stream of bits (all zeroes or all ones) is totally predictable (has no entropy). Maximum entropy occurs when a stream of data has totally unpredictable bits. Entropy is a measure of the unpredictability of an information stream. Huffman coding is an entropy encoding algorithm used for lossless data compression. The shortest codes are assigned to the most frequent characters and the longest codes are assigned to infrequent characters. Huffman coding & deciding algorithm is used in compressing data with variable-length codes. Some programs use just the Huffman coding method, while others use it as one step in a multistep compression process. It is widely used in all the mainstream compression formats that you might encounter-from GZIP, PKZIP (winzip, etc) and BZIP2, to image formats such as JPEG and PNG.

Huffman algorithm is a popular encoding method used in electronics communication systems. The MATLAB program presented here encodes and decodes the information and also outputs the values of entropy, efficiency and frequency probabilities of characters present in the data stream. Encoding the information before transmission is necessary to ensure data security and efficient delivery of the information.

How to encode a message into an image matlab