Sequential scalar quantization of color images

Abstract

An efficient algorithm for color image quantization is proposed based on a new vector quantization technique that we call sequential scalar quantization. The scalar components of the 3-D color vector are individually quantized in a predetermined sequence. With this technique, the color palette is designed very efficiently, while pixel mapping is performed with no computation. To obtain an optimal allocation of quantization levels along each color coordinate, we appeal to the asymptotic quantization theory, where the number of quantization levels is assumed to be very large. We modify this theory to suit our application, where the number of quantization 1evels is typically small. To utilize the properties of the human visual system (HVS), the quantization is performed in a luminance-chrominance color space. A luminance-chrominance weighting is introduced to account for the greater sensitivity of the HVS to luminance than to chrominance errors. A spatial activity measure is also incorporated to reflect the increased sensitivity of the HVS to quantization errors in smooth image regions. The algorithm yields high-quality images and is significantly faster than existing quantization algorithms.