es), are concentrated in the lower right. Since there are usually more flat surfaces than sharp edges in a picture, the first few values will probably be non-zero. However, as the zigzag scanning continues the frequency increases. The coefficients produced become smaller and smaller. By the time the sixty-fourth value is measured, it is an expression of the magnitude of the Coding and Compression highest frequency in both the horizontal and vertical planes-a value that tends to zero after the quantization process is applied. The resulting 64 DCT values are arranged in a matrix. This all occurs before the actual compression process; what started as 64 eight-bit pixels resulted in 64 eight-bit coefficient values, most of which are zero or almost zero. In the process of quantization, the almost-zero values become zero; they are not important because the human eye would not perceive them anyway. The most important value, the DC coefficient, is quantized and coded using an 8-bit code. After quantization, a bit stream is formed from the pixel block. This bit stream is ready for compression. Compression in H.261 and H.263 codecs takes the form of run-length and Huffman encoding. In run length encoding, the codec compresses a string of identical bits by sending a bit pattern followed by a token that indicates the number of times the pattern repeats. Huffman encoding then converts the most frequently repeated tokens into the shortest bit strings. To summarize, the human eye, like the human ear, is more responsive to low frequencies than to high ones. In audio companding, PCM codes low frequencies with more precision than higher ones. DCT works the same way. The low frequencies in image encoding contain the picture's detail. DCT compresses by giving priority to that which humans need most for image processing. VECTOR QUANTIZATION Used in a number of proprietary compression techniques (most notably, Intel's Indeo) vector quantization is a less compute-intensive alternative to the DCT. Vector quantization is a lossy compress