s used in the MPEG-1 information content using the number of bits per symbol and H.261 standards to encode the set of quantized DC or bit rate. The entropy of a digital source is further de- coefficients generated by the discrete cosine transforms. fined as the average information content of the source, i.e., The DPCM predictive encoder modifies the use of the Markov source model considerably in order to reduce its complexity. It does not rely on the actual Markov source statistics at all, and simply creates a linear weighting of the last m symbols (m-th order) to bits/symbol. (7.4.5) predict the next state. This significantly reduces the complexity of using Markov source prediction at the expense of an increase in the bit rate. DPCM encodes This relationship suggests that the average number of the differential signal d between the actual value and bits per symbol required to represent the information the predicted value, i.e., d = e - e, where the pre- content of the source is the entropy. The noiseless source diction e is a linear weighting of m previous values. The coding theorem states that a source can be encoded with resulting differential signal d generally has reduced an average number of bits per source symbol that is entropy as compared to the original source. DPCM is arbitrarily close to the source entropy. So called entropy used in conjunction with a VLC encoder to reduce the encoders seek to find codes that perform close to the bit rate. The simplicity and entropy reduction capa- entropy of the source. Huffman and arithmetic encoders bility of DPCM makes it a good choice for use in real- are examples of entropy encoders. time compression systems. Third order predictors (m Modified Huffman coding [9] is commonly used in = 3) have been shown to provide good performance on the image and video compression standards. It produces natural images [10]. good performing variable length codes without significant computational complexity. The traditional Huffman algorithm is a two step process that first creates