Multispectral remotely sensed images interpretation using fuzzy neural networks

International audienceMultispectral remotely sensed images interpretation using fuzzy neural network

An optimization of finite state vector quantization for image compression

International audienceThis paper focuses on the conditional histogram (CH) next-state function design used for the finite-state vector quantization (FSVQ) image compression approach. A new coding scheme is proposed which optimizes the performance of CH while ensuring the same reconstruction quality as that of the full-search VQ. The optimization is performed by determining for every input block the subcodebook size that minimizes the expected value of the number of bits in the compressed bit-flow. Two different algorithms are studied in order to ensure the best reconstruction. The proposed scheme is shown to give better results than classical FSVQ approaches. In fact, the proposed approach reveals the relationship between FSVQ and conditional entropy-coded VQ scheme