Quantization based on a novel sample-adaptive product quantizer (SAPQ)

In this paper we propose a novel feedforward adaptive quantization scheme called the sample-adaptive product quantizer (SAPQ). This is a structurally constrained vector quantizer that uses unions of product codebooks. SAPQ is based on a concept of adaptive quantization to the varying samples of the source and is very different from traditional adaptation techniques for nonstationary sources. SAPQ quantizes each source sample using a sequence of quantizers, Even when using scaler quantization in SAPQ, we can achieve performance comparable to vector quantization (with. the complexing still close to that of scalar quantization), We also show that important lattice-based vector quantizers can be constructed using scalar quantization in SAPQ. We mathematically analyze SAPQ and propose a simple algorithm to implement it, We numerically study SAPQ for independent and identically distributed Gaussian and Laplacian sources. Through our numerical study, me find that SAPQ using scalar quantizers achieves typical gains of 1-3 dB in distortion over the LIoyd-Max quantizer. We also show that SAPQ can be used in conjunction with vector quantizers to further improve the gains.