Distortion equalized fuzzy competitive learning for image data vector quantization

Abstract

Vector quantization is a popular approach to image compression as it allows images to be coded at less than one bit per pixel. This paper presents a modified fuzzy competitive learning algorithm and applies it to image data vector quantization. The proposed algorithm overcomes the neuron underutilization problem by applying both fuzzy learning and distortion equalization to the competitive learning algorithm. Experimental results on real image data shows that this approach produces a higher quality codebook than applying fuzzy learning or distortion equalization to the competitive learning algorithm individually.