niques eliminate signal duplication by using shorthand methods that the coding and decoding devices on each end readily understand. The only part of the signal that must be fully described is the part that is random, and is thereby impossible to predict. Redundancy-elimination compression first worked on achieving entropy in text messages. Text files consist of only about 50 different characters, some of which Coding and Compression appear often. The letters E, T, o, and I, for instance, collectively account for 37% of all character appearances in an average text file. David Huffman, a researcher at MIT, noted and subsequently exploited their repetition. In 1960, Huffman devised the first text compression algorithm. The idea behind Huffman encoding is similar to that of Morse code. It assigns short, simple codes to common characters or sequences and longer more complex ones to those that appear infrequently. Huffman encoding can reduce a text file by approximately 40%. A lossless compression technique, it is used to reduce long stings of zeros in many standards-based video compression systems including H.261, H.263, and MPEG. Other lossless compression techniques followed. Developed in the 1970's, Lempel- Ziv-Welch (LZW) compression focused not on the characters themselves, but on repetitive bit combinations. LZW coding builds a dictionary of these commonly used data sequences and represents them with abbreviated codes. Originally developed for text, another technique called run-length coding, compresses at the bit level by reducing the number of zeros and ones in a file. Sending only one example, followed by a shorthand description of the number of times it repeats, indicates a string of identical bits. When LZW and run-length coding are used in video compression, it is generally after lossy techniques have done their job. Compressing moving images is much more demanding than compressing text. A frame of video contains vastly more information. Fortunately, there is usually a correlation between neighboring pixel