Nowadays Internet can be considered has the cne of most instrument of communication. They are two important exigencies that are strongly been demanded by the digital imaging market: progressiveness, which is requested by the tremendous market of the world wide web, and resolution independence, which is a key requirement for desktop publishing. A hybrid fractal wavelet Trellis -Code Quantization (TCQ) method is proposed for transmission of compression image to satisfy these two requirements. The high compression and the resolution independency are assured the Hybrid Fractal-Wavelet method, and the TCQ the progressiveness in transmission, by quantize the residual error in the encoding. The efficiency of our method has been proved comparison with the common Fractal-Wavelet method.
Finding the optimal algorithm between an efficient encoding process and the rate distortion is the main research in fractal image compression theory. A new method has been proposed in this paper based on the optimization ofthe LeastSquare Error and the orthogonal projection. A large number ofdomain blocks can be eliminated, in order to speed-up fractal image compression. Moreover, since the rate-distortion performance ofthe most fractal image coders is not satisfactory, a n efficient bit allocation algorithm to improve the rate distortion is also proposed. Some implementation and comparison have been done with the feature extraction method to prove the efficiency ofthe proposed method.
Recent developments in Pulse-Coupled Neural Networks (PCNN) techniques provide efficiency in edge and target extraction. The detection of targets is facilitated by PCNN multi-scale image factorization. But noise is still the enemy of PCNN. An efficient new Pulse-Coupled Neural Networks technique has been proposed in combination with the wavelet theory. The new Pulse-Coupled Neural Network Wavelet (PCNNW) is based on multi-resolution decomposition for extracting the main features of the images by eliminating the noise. In addition, the wavelet coefficients provide the Pulse-Coupled Neural Network (PCNN) supplemental discrimination and lead to characteristic sets of numbers useful in identifying image factors of interest. The efficiency of the method has been tested and compared with other PCNN denoising methods.
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