FINITE STATE VECTOR QUANTIZATION OF IMAGE BY NEURAL NETWORKS

Rastislav Labovský - Ján Mihalík

   The presented paper deals with finite state vector quantization of an image, where neural networks are applied. The system of finite state vector quantization of an image is based on an idea to select a sub-codebook from the super-codebook, dynamically for each input vector. The result of this is exploiting of the high performance of the super-codebook (low mean square quantization error) but using a low bit rate that is necessary to code the sub-codebook. The super-codebook was designed by a neural network clustering algorithm. We have implemented the selection of the sub-codebook from the super-codebook by a non-linear neural network vector predictor. The vector predictor was realised by three-layer perceptron with a hidden layer, sigmoid and bias units, where its optimization is based on an error back-propagation learning algorithm. We have designed two systems of finite state vector quantizer, the first one with a fixed length of codewords, the second one with a variable length of codewords. Finally we applied the systems on coding of image Lena of size 512x512 pels for different bit rates, where we have used one-dimensional and two-dimensional neural network vector prediction of states and a vector quantizer on the basis on neural networks.

Keywords: segmentation, vector quantization, vector prediction, neural networks

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