Image Compression/Reconstruction Based on Various Types of Fuzzy Relational Equations
Hajime Nobuhara, Yasufumi Takama, Kaoru Hirota · IEEJ Transactions on Electronics Information and Systems · 2001
A fast image compression and reconstruction method is proposed, based on various types of fuzzy relational equations, i.e., max-t-norm, adjoint max-t-norm, min-s-norm, and adjoint min-s-norm composite fuzzy relational equation, where the quality of reconstructed images is also improved. The experiment using 20 images from a standard image database (SIDBA) confirms the decrease in the compression (≈ 1/300) and reconstruction (≈ 1/310) times, as well as in the root mean square error (≈ 1/2), compared with the results of Hirota & Pedrycz (1999). It is shown by the wavelet analysis that the overall appearance of the reconstructed images is comparable with those of the discrete cosine transform method.