An efficient Euclidean distance computation for vector quantization using a truncated look-up table

Vector quantizer (VQ) encoders generally use Euclidean distance measure to encode the vectors. The major computation in the Euclidean distance is square (multiplication operation) of the difference between the vector components. This article explores Euclidean distance computation and introduces a new technique which uses a truncated look-up table (LUT) to store a small set of repeatedly generated scalars. Specifically, for numbers represented by m bits, this technique requires to store only 2^m product terms instead of 2^m ×2^m product terms needed to store in a conventional LUT