ion method that uses a small set of data to represent a larger one. Stated differently, codecs that use vector quantization share a codebook. This codebook contains an array of possible values, and shorthand codes for conveying them. As the name implies, vector quantization quantizes vectors. A vector is a mathematical representation of a force. In video coding, this force represents a color (in other words, a frequency). Not only must we consider frequency when dealing with images, we must also consider the intensity of that frequency (its amplitude). Luminance determines amplitude, the relative saturation of a pixel's color. In vector quantization, we analyze a block of pixels to discover its vector. This vector is then used to select a predefined equation that describes it in a more efficient way. Codecs can be tuned SO that the quantization process considers, not just a pixel block's vector, but also how this vector relates to those of surrounding blocks within the image. In most pictures, colors (and their corresponding saturation) are concentrated in certain areas. These colors are associated with objects (e.g., walls, white boards, clothing, and paper). A codec can select a relatively small subset of the Video Communications: The Whole Picture representative vectors in an image and use this subset to approximate the vectors of all other pixel blocks. If a pixel block's vector, once measured, falls into the "red" codebook, it can be described using an abbreviated code. That code, which might be only a few bits long, is interpreted by the decoder as a "red" with a given hue and saturation. VIDEO COMPRESSION Video compression techniques do not strive for total quality, just something good enough. Good enough, of course, depends on the application, and quality is, therefore, subjective. However, all types of lossy compression leverage the human tendency to consider context and thereby interpret what one sees or hears when minor bits of information are missing. Video compression techniques exploit the functioni