Vector quantization

Vector Quantization (VQ) is a lossy compression technique used to reduce the data size of a source signal by replacing it with a set of symbols known as “codewords”. VQ finds the best (optimal) match of individual codewords representing different values of a given signal vector. Each codeword or symbol represents a discrete set of data points in the vector that can be used to reconstruct the original data. In other words, it is a data compression technique where the amount of data is reduced without significant loss of information.

In VQ, the source signal is first split into individual data points. These data points are then compared to the codewords (known as vectors) to find the codeword that most closely matches. The best matching codeword is then selected and substituted for that data point. This process is repeated until the entire vector is reduced down into a series of codewords.

VQ has been used in a variety of applications, such as image and video compression, speech recognition, and audio signal processing. It is particularly handy for situations where the data is already in a predefined vector format, such as a two-dimensional grid. VQ can also be used in data mining and machine learning, where it can be used to learn patterns in large datasets.

VQ is also known as codebook design, vector quantization, vector quantizer design, vector quantization coding, and codebook vector quantization. It is an important concept in the fields of digital signal processing, pattern recognition, and data compression.