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Extended abstractIn this paper, we study the filters' effects in Image compression by wavelet transform. The principle of wavelet transform is to decompose hierchically the input image into a series of successively lower resolution reference images and detail images which contain the information needed to be reconstructed back to the next higher resolution level.The histogram of image sub-bands provides us with information on the distribution of the coefficient values in this sub-image. The sub-band images resulting from wavelet transform are not equal significance. Some sub-bands contain more information than others. The total number of available bits describing an image is however inevitably limited. Therefore, it is desirable to allocate more bits to those sub-bands images which can be coded accurately than others. The objective of a such bit allocation method is to optimize the overall coder performance and minimize the quantization error. In determining which wavelet filter is to be used for image compression, some of the properties considered are vanishing moments. The phase non-linearity of the filter can cause severe degradation in the subjective quality of an image. It is related to the symmetry of the filter coefficients. The wavelet transform is implemend using a linear-phase. Biorthogonal filter with four levels of decomposition.For this study, we use a scalar quantization with uniform threshold quantizers. The quantization method is PCM (Impulse Coded Modulation) for the coefficients in all high-pass sub-bands. The coefficients of low-pass sub-bands are DPCM (Differential PCM) quantized per region.Suitable criteria are needed to evaluate rigorously the performance of e compression scheme. In case of images, the search for simple and suitable criteria is hindrcd by the fact that the results obtained by statistical performance criteria may not agree with subjective evaluation of the humain eye. Since the objective is data compression, the compression ratio at optimal distorsion is obviously an important performance measure. The reconstructed imae quality can be evaluated by mean-squared error (MSE) and peak signal to noise ratio (PSNR).We can subdivide the image in one or mode sub-images ; if we increase the level of decomposition, the compression quality ("PSNR) will improve.From this study, we can conclude that the PSNR is very similar when the level of decomposition is greater than 4. The biorthogonal filters provide us good results in image compression.Finally, the filters' effects, like those of the decomposition levels, on the compression of the images by wavelets, are very significants. The best choice of the filter and decomposition levels can ensure us a best quality of compression.

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In this paper, a new approach of images coding by Shapiro algorithm (Embedded Zerotree Wavelet algorithm or EZW) is proposed. This approach, the modified EZW (MEZW), distributes entropy differently than Shapiro's and also optimizes the coding. This can produce results that are a significant improvement on the PSNR and compression ratio obtained by Shapiro, without affecting the computing time. These results are also comparable with those obtained using the SPIHT and SPECK algorithms. The EZW, Spiht or Speck algorithms are based on the Wavelet transform. The principle ofwavelet transform is to decompose hierarchically the input image into a series ofsuccessively lower resolution reference images and detail images which contain the information needed to be reconstructed back to the next higher resolution level. The sub-band images resulting from wavelet transform are not ofequal significance. Some sub-bands contain more information than others (example the baseband sub band).