In this paper new semilogarithmic quantizer for Laplacian distribution is presented. It is simpler than classic A-law semilogarithmic quantizer since it has unit gain around zero. Also, it gives for 2 97 dB higher sig...
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In this paper new semilogarithmic quantizer for Laplacian distribution is presented. It is simpler than classic A-law semilogarithmic quantizer since it has unit gain around zero. Also, it gives for 2 97 dB higher signal-to-quantization noise-ratio (SQNR) for referent variance in relation to A-law, and therefore it is more suitable for adaptation Forward adaptation of this quantizer is done on frame-by-frame basis. In this way G 712 standard is satisfied with 7 bits/sample. which is not possible with classic A-law. Inside each frame subframes are formed and lossless encoder is applied on subframes. In that way, double adaptation is done. adaptation on variance within frames and adaptation on amplitude within subframes. Joined design of quantizer and lossless encoder is done, which gives better performances. As a result, standard G 712 is satisfied with only 6.43 bits/sample. Experimental results, obtained by applying this model on speech signal, are presented. It is shown that experimental and theoretical results are matched very well (difference is less than 1 5%) Models presented in this paper can be applied for speech signal and any other signal with Laplacian distribution.
In pyramidal wavelet representation, an image is decomposed into multiresolution and multifrequency subbands with sets of tree-structured coefficients, Le. a spatial orientation tree which consists of coefficients at ...
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In pyramidal wavelet representation, an image is decomposed into multiresolution and multifrequency subbands with sets of tree-structured coefficients, Le. a spatial orientation tree which consists of coefficients at different resolutions and different orientations but associated with the same spatial location. The magnitudes of the coefficients in these trees measure the signal activity level of the corresponding spatial areas. A novel coefficient partitioning algorithm is introduced for splitting the coefficients into two sets using a spatial orientation tree data structure. By splitting the coefficients, the overall theoretical entropy is reduced due to the different probability distributions for the two coefficient sets. In the spatial domain, it is equivalent to identifying smooth regions of the image. A lossless coder based on this spatial coefficient partitioning has a better coding performance than other wavelet-based lossless image coders such as S + P and JPEG-2000.
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