Stochastic Optimization of Vector Quantization Methods in Application to Speech and Image Processing

Vector quantization (VQ) methods have been used in a wide range of applications for speech, image, and video data. While classic VQ methods often use expectation maximization, in this paper, we investigate the use of stochastic optimization employing our recently proposed noise substitution in vector quantization technique. We consider three variants of VQ including additive VQ, residual VQ, and product VQ, and evaluate their quality, complexity and bitrate in speech coding, image compression, approximate nearest neighbor search, and a selection of toy examples. Our experimental results demonstrate the trade-offs in accuracy, complexity, and bitrate such that using our open source implementations and complexity calculator, the best vector quantization method can be chosen for a particular problem.