Abstract
In this paper optimal image quantization algorithms and their dependence on initial conditions are studied. For gray-level images using four different types of initial conditions, the behaviour of the well-known optimal Lloyd-Max quantization (LMQ) algorithm is studied and compared to a fuzzy version (FLMQ). A generic quantization algorithm is developed which is a hybrid approach combining a genetic algorithm with optimal quantization. It is shown that the latter technique is almost insensitive to initial conditions and performs better than the former two. For color images the technique is shown to lead to visual improvement of the image quality.
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