Data compression algorithm geeksforgeeks. Compression techniques are essential for efficient data storage and transmission. Understanding the differences between these strategies is critical for selecting the best solution depending on the unique requirements of various applications. Data compression can be applied to text, pictures, audio and video among other forms of data. There are two forms of compression: lossless and lossy. Compression techniques are essential for efficient data storage and transmission. . This concept involves minimizing redundancies and irrelevances in representing data so that less bytes or bits are required to store or convey it. In computer science and information theory, a Huffman code is a particular type of optimal prefix code that is commonly used for lossless data compression. It has been under development since either 1996 or 1998 by Igor Pavlov [1] and was first used in the 7z format of the 7-Zip archiver. Named after Claude Shannon and Robert Fano, it assigns a code to each symbol based on their probabilities of occurrence. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. The Lempel–Ziv–Markov chain algorithm (LZMA) is an algorithm used to perform lossless data compression. Shannon Fano Algorithm is an entropy encoding technique for lossless data compression of multimedia. Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. In this post we are going to explore LZ77, a lossless data-compression algorithm created by Lempel and Ziv in 1977. This algorithm is widely spread in our current systems since, for instance, ZIP and GZIP are based on LZ77. zwsyed iuplx tykrly rwwboo bzetzocp fgihpu fiuwgzf wfynd wny lqydd