Vector quantization (VQ) is a form of data compression used in digital communication and data storage. It is a form of lossy data compression, meaning that some of the original data is lost as a result of the compression process. Vector quantization works by dividing the data into a series of fixed-length vectors, which are then encoded using a set of codebook vectors. These codebook vectors are selected from the input data set and represent a subset of all the possible vectors that can be created from the data set.
Vector quantization is used in numerous applications, such as audio and image compression, video coding, and data transmission. VQ can be used to reduce the amount of data that is transmitted over a communication medium or stored in a database. Vector quantization can also be used to extract meaningful features from an image or signal.
The process of vector quantization begins with the selection of an appropriate codebook. This codebook is typically generated using a clustering algorithm, which groups similar vectors together and assigns them a representative codebook vector. Once the codebook has been generated, the data set is split into fixed-length vectors, and each vector is then compared to the codebook. The codebook vector that is closest in distance is assigned to the fixed-length vector. This process is repeated until all of the data has been encoded.
Vector quantization is often used to reduce the time and space complexity of data storage and communication. It is also used to improve the transmission of sensitive materials such as audio and video, as it reduces the amount of data that needs to be transmitted. By reducing the size of data, the amount of time required for transmitting and decoding the data is greatly reduced.