Adaptive quantization using piecewise companding and scaling for Gaussian mixture

Quantization is fundamental to analog-to-digital converter (ADC) and signal compression. In this paper, we propose an adaptive quantizer with piecewise companding and scaling for signals of Gaussian mixture model (GMM). Our adaptive quantizer operates under three modes, each of which corresponds to different types of GMM. Moreover, we propose a reconfigurable architecture to implement our adaptive quantizer in an ADC. We also use it to quantize images and design the tone mapping algorithm for high dynamic range (HDR) image compression. Our experimental results show that (1) the proposed quantizer is able to achieve performance close to the optimal quantizer (i.e., Lloyd–Max quantizer for GMM) in the sense of mean squared error (MSE), at much lower computational cost than it; (2) the proposed quantizer is able to achieve much better MSE performance than a uniform quantizer, at a cost similar to the uniform quantizer. The proposed adaptive quantizer holds great potential in the appilcations of the existing ADC and HDR image compression.

► Propose a suboptimal adaptive quantizer by signal modelling and the scaling law.
► Apply it to an ADC, image quantization and high dynamic range image compression.
► Get performance close to Lloyd–Max quantizer at lower computational cost than it.
► Achieve much better MSE performance than a uniform quantizer, at a cost similar to it.
► Get good perceptual quality of quantized images from grey images and HDRIs.