Vector quantization and signal compression
Vector quantization and signal compression
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An image coding/decoding method based on direct and inverse fuzzy transforms
International Journal of Approximate Reasoning
Rough fuzzy set based scale space transforms and their use in image analysis
International Journal of Approximate Reasoning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A methodology for constructing fuzzy algorithms for learning vector quantization
IEEE Transactions on Neural Networks
An axiomatic approach to soft learning vector quantization and clustering
IEEE Transactions on Neural Networks
Extended rough set-based attribute reduction in inconsistent incomplete decision systems
Information Sciences: an International Journal
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A new coding/decoding scheme based on the properties and operations of rough fuzzy sets is presented. By normalizing pixel values of an image, each pixel value can be interpreted as the degree of belonging of that pixel to the image foreground. The image is then subdivided into blocks which are partitioned and characterized by a pair of approximation sets. Coding uses a codebook, created with a quantization algorithm, to find the best approximating pair for each block, while decoding exploits specific properties of rough fuzzy sets to rebuild the blocks. The method, called by us rough fuzzy vector quantization (RFVQ) relies on the representation capabilities of the vector to be quantized and not on the quantization algorithm, to determine optimal codevectors. A comparison with other fuzzy-based coding/decoding schemes and with DCT and JPEG methods is performed by means of peak signal to noise ratio (PSNR) values. Results show that for low compression rates the proposed method performs well and, in some cases, the PSNR obtained with RFVQ is close to the JPEG's PSNR.