on-line signature recognition. In pattern recognition applications, one codebook is constructed for each class (each class being a user in biometric applications) using acoustic vectors of this user. In the testing phase the quantization distortion of a testing signal is worked out with the whole set of codebooks obtained in the training phase. The codebook that provides the smallest vector quantization distortion indicates the identified user. The main advantage of VQ in pattern recognition is its low computational burden when compared with other techniques such as dynamic time warping (DTW) and hidden Markov model (HMM). The main drawback when compared to DTW and HMM is that it does not take into account the temporal evolution of the signals (speech, signature, etc.) because all the vectors are mixed up. In order to overcome this problem a multi-section codebook approach has been proposed. The multi-section approach consists of modelling the signal with several sections (for instance, one codebook for the initial part, another one for the center and a last codebook for the ending part). Use as clustering algorithm As VQ is seeking for centroids as density points of nearby lying samples, it can be also directly used as a prototype-based clustering method: each centroid is then associated with one prototype. By aiming to minimize the expected squared quantization error and introducing a decreasing learning gain fulfilling the Robbins-Monro conditions, multiple iterations over the whole data set with a concrete but fixed number of prototypes converges to the solution of k-means clustering algorithm in an incremental manner. Generative Adversarial Networks (GAN) VQ has been used to quantize a feature representation layer in the discriminator of Generative adversarial networks. The feature quantization (FQ) technique performs implicit feature matching. It improves the GAN training, and yields an improved performance on a variety of popular GAN models: BigGAN for image generation, StyleGAN for face synthesis, and U-GAT-IT for unsupervised image-to-image translation. See also Speech coding Ogg Vorbis Voronoi diagram Rate-distortion function Data clustering Learning vector quantization Centroidal Voronoi tessellation Growing Neural Gas, a neural network-like system for vector quantization Image segmentation Lloyd's algorithm Linde,Buzo,Gray Algorithm (LBG) K-means clustering Autoencoder Deep Learning Part of this article was originally based on material from the Free On-line Dictionary of Computing and is used with permission under the GFDL. References External links http://www.data-compression.com/vq.html QccPack — Quantization, Compression, and Coding Library (open source) VQ Indexes Compression and Information Hiding Using