Image Compression using Huffman Coding Scheme with Partial/Piecewise Color Selection

Compression is the specialty of presenting the data in a conservative structure as opposed to its unique or uncompressed structure. Moreover, utilizing information compression, the size of a specific document can be decreased. This is extremely helpful when preparing, putting away, or moving a gigantic document, which needs huge resources. The speed of transmission relies on the number of pieces sent, the time needed for the encoder to create the coded message, and the time needed for the decoder to recoup the primary ensemble. In an information storage application, the level of compression is the essential concern. Compression can be named either lossless or lossy. Lossless compression methods remake the primary data from the compacted record with no loss of information. In this manner, the data does not alter during the decompression and compression measures. These sorts of compression calculations are called reversible compressions since the primary message is recreated by the decompression cycle. This paper analyzes the exhibition of the Huffman Encoding Algorithm, Lempel Zev Welch Algorithm, Arithmetic Encoding Algorithm, Adaptive Huffman Encoding Algorithm, Shannon Fano Algorithm, and Run Length Encoding Algorithm. Specifically, the efficiency of these calculations in compacting text information is compressed and assessed.