Colour quantisation technique based on image decomposition and its embedded system implementation

Abstract

A new colour quantisation (CQ) technique and its corresponding embedded system realisation are introduced. The CQ technique is based on image split into sub-images and the use of Kohonen self-organised neural network classifiers (SONNC). Initially, the dominant colours of each sub-image are extracted through SONNCs and then are used for the quantisation of the colours of the entire image. The proposed CQ technique can use both colour components and spatial features, achieving better approximation of the final image to the spatial characteristics of the original one. In addition, for the estimation of the proper number of dominant image colours, a new algorithm based on the projection of the image colours into the first two principal components is proposed. The image split into sub-images offers reduction of the on-chip memory requirements and is suitable for embedded system (or system-on-chip) implementation of the most time-consuming part of the technique. Applying a systematic design methodology to the developed CQ algorithm, an efficient embedded architecture based on the ARM7 processor achieving high-speed processing and less energy consumption, is derived.