Fuzzy Relation Equations and Their Applications to Knowledge Engineering
Fuzzy Relation Equations and Their Applications to Knowledge Engineering
Data compression with fuzzy relational equations
Fuzzy Sets and Systems - Information processing
Fuzzy relation equations for coding/decoding processes of images and videos
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy Optimization and Decision Making
Relational image compression: optimizations through the design of fuzzy coders and YUV color space
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Fuzzy transforms: Theory and applications
Fuzzy Sets and Systems
A method for coding/decoding images by using fuzzy relation equations
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
A motion compression/reconstruction method based on max t-norm composite fuzzy relational equations
Information Sciences: an International Journal
IEEE Transactions on Fuzzy Systems
Rough fuzzy set-based image compression
Fuzzy Sets and Systems
A segmentation method for images compressed by fuzzy transforms
Fuzzy Sets and Systems
Fuzzy transforms method and attribute dependency in data analysis
Information Sciences: an International Journal
Approximation of extensional fuzzy relations over a residuated lattice
Fuzzy Sets and Systems
Fuzzy transforms of monotone functions with application to image compression
Information Sciences: an International Journal
Fuzzy transforms for compression and decompression of color videos
Information Sciences: an International Journal
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Social interactions in Ambient Intelligent environments
Journal of Ambient Intelligence and Smart Environments
Fuzzy transforms method in prediction data analysis
Fuzzy Sets and Systems
Fuzzy transform and least-squares approximation: Analogies, differences, and generalizations
Fuzzy Sets and Systems
A smoothing filter based on fuzzy transform
Fuzzy Sets and Systems
Approximation properties of fuzzy transforms
Fuzzy Sets and Systems
Supporting trading strategies by inverse fuzzy transform
Fuzzy Sets and Systems
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms: Theoretical Aspects and Applications to Fuzzy Systems
Fuzzy projection versus inverse fuzzy transform as sampling/interpolation schemes
Fuzzy Sets and Systems
Fragile watermarking tamper detection with images compressed by fuzzy transform
Information Sciences: an International Journal
Advanced F-transform-based image fusion
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms (2012)
Detection of fuzzy association rules by fuzzy transforms
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms (2012)
Image matching by using fuzzy transforms
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms 2013
Coding B-frames of color videos with fuzzy transforms
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms 2013
Generating suitable basic functions used in image reconstruction by f-transform
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms 2013
A color image reduction based on fuzzy transforms
Information Sciences: an International Journal
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With some modifications, we adopt the coding/decoding method of image processing based on the direct and inverse fuzzy transforms defined in previous papers. By normalizing the values of its pixels, any image can be considered as a fuzzy matrix (relation) which is subdivided in submatrices (possibly square) called blocks. Each block is compressed with the formula of the discrete fuzzy transform of a function in two variables and successively it is decompressed via the related inverse fuzzy transform. The decompressed blocks are recomposed for the reconstruction of the image, whose quality is evaluated by calculating the PSNR (Peak Signal to Noise Ratio) with respect to the original image. A comparison with the coding/decoding method of image processing based on the fuzzy relation equations with the Lukasiewicz triangular norm and the DCT method are also presented. By using the same compression rate in the three methods, the results show that the PSNR obtained with the usage of direct and inverse fuzzy transforms is higher than the PSNR determined either with fuzzy relation equations method or in the DCT one and it is close to the PSNR determined in JPEG method for small values of the compression rate.