Increasing the Error Tolerance in Transmission of Vector Quantized Images by Self-organizing Map 1. Image Vector Quantization Using Self-organizing Maps

Transmission of Vector Quantized Images by Self-Organizing Map Jari Kangas Helsinki University of Technology Neural Networks Research Centre Rakentajanaukio 2 C, FIN-02150, Espoo, FINLAND tel: +358 0 451 3275, fax: +358 0 451 3277 email: Jari.Kangas@hut. Abstract Image compression is needed for image storage and transmission applications. Vector quantization methods o er good performance when high compression rates are needed. Image quality problems may be encountered if vector quantization methods are used in transmission of images through noisy transmission channels, because erroneous codeword will usually be decoded into a whole block of completely erroneous pixels. Image quality can be significantly improved if noise properties are taken into account already in VQ design stage. In this paper it is shown that using the Self-Organizing Map algorithm in vector quantization codebook design one is able to reduce the degradation due to transmission errors. 1. Image Vector Quantization using Self-Organizing Maps Image compression is needed for image storage and transmission applications [1]. Lately the image compression using Vector Quantization (VQ) techniques has received large interest [2]. In VQ approaches adjacent pixels are taken as a single block, which is mapped into a nite set of codewords. In decoding stage the codewords are replaced by corresponding model vectors (see Figure 1). The set of codewords and the associated model vectors together is called a codebook. In VQ the correlations which exists between adjacent pixels are naturally taken into account, and with a comparatively small codebook one achieves a small quantization error in reconstructed images.