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
Compression and decompression of images with discrete fuzzy transforms
Information Sciences: an International Journal
Fuzzy transform in the analysis of data
International Journal of Approximate Reasoning
An image coding/decoding method based on direct and inverse fuzzy transforms
International Journal of Approximate Reasoning
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
Fuzzy transforms: Theory and applications
Fuzzy Sets and Systems
Aggregation functions based on penalties
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 transforms method in prediction data analysis
Fuzzy Sets and Systems
A motion compression/reconstruction method based on max t-norm composite fuzzy relational equations
Information Sciences: an International Journal
Fragile watermarking tamper detection with images compressed by fuzzy transform
Information Sciences: an International Journal
Fuzzy transforms and their applications to image compression
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Image Reduction Using Means on Discrete Product Lattices
IEEE Transactions on Image Processing
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We present a new method for color image reduction based on the concept of fuzzy transform. Any image in a single band can be considered as a fuzzy matrix which is subdivided into submatrices called blocks. Each block is compressed with various_compression rates by means of a fuzzy transform in two variables. We compare our method with recent three algorithms due to G. Beliakov, H. Bustince and D. Paternain based on the minimizing penalty functions defined over a discrete lattice. The quality of the reduced image is measured by the Mean Square Error (MSE) and Penalty function (PEN) obtained by comparing both magnified and original images. We also point out a threshold of the compression rate beyond which the MSE follows a linear trend and the corresponding loss of information is still acceptable.