Vector quantization, also called "block quantization" or "pattern matching quantization" is often used in lossy data compression. It works by encoding values from a multidimensional vector space into a finite set of values from a discrete subspace of lower dimension. A lower-space vector requires less storage space, so the data is compressed. Due to the density matching property of vector quantization, the compressed data has errors that are inversely proportional to density.
Vector quantization also called "block quantization" or "pattern matching quantization" is often used in lossy data compression It works by encoding values from a multidimensional vector space into a finite set of values from a discrete subspace of lower dimension A lower-space vector requires less storage space so the data is compressed Due to the density matching property of vector quantization the compressed data has errors that are inversely proportional to density