Vektorikoodit

Vektorikoodit, sometimes referred to as vector quantization, is a data compression technique used in signal processing and data science. It is a form of lossy compression, meaning that some information is lost during the compression process. The core idea is to represent a large set of data points, often vectors, with a smaller set of representative vectors called codewords. These codewords form a codebook.

The process begins by creating a codebook of representative vectors. This is typically done by analyzing a training dataset of vectors and grouping similar vectors together. The centroid of each group becomes a codeword in the codebook. Once the codebook is established, any new vector from the data can be compressed by finding the closest codeword in the codebook and replacing the original vector with the index of that codeword.

Decoding involves retrieving the original vector by looking up the codeword corresponding to the received index in the codebook. The quality of the compression depends on the size and representativeness of the codebook. Larger codebooks generally lead to better fidelity but also higher storage requirements. Vektorikoodit finds applications in areas such as speech coding, image compression, and pattern recognition, where efficient representation of complex data is crucial. It is a foundational concept in many modern compression algorithms.