This paper proposes particle swarm optimization method to design M channel near perfect reconstruction pseudo QMF banks used in transforming stage of image coder. The filter bank is designed to have highest entropy based coder. To achieve... more
This paper proposes particle swarm optimization method to design M channel near perfect reconstruction pseudo QMF banks used in transforming stage of image coder. The filter bank is designed to have highest entropy based coder. To achieve high energy compaction and least distortion, design problem is formulated as a combination of the coding gain, low dc leakage conditions and stopband attenuation. For distortion free signal representation perfect reconstruction and good visual quality measures are imposed as constraints. The design problem is solved using (particle swarm optimization) PSO technique for minimizing filter tap weights. The technique find out solution by searching feasible solutions that achieve the best solution for the objectives criteria mentioned above. The performance of this optimization technique in filter bank design for image compression is evaluated in terms of both objective quality via coding gain, PSNR measures and subjective visual quality measure using both JPEG baseline image coder and an Embedded Zerotree Wavelet (EZW) coder. For comparison same test images for approximately same conditions and characteristics are used to measure compression ratio and peak signal to noise ratio (PSNR) for lower bit rates.
In the present article we introduce and validate an approach for single-label multi-class document categorization based on text content features. The introduced approach uses the statistical property of Principal Component Analysis, which... more
In the present article we introduce and validate an approach for single-label multi-class document categorization based on text content features. The introduced approach uses the statistical property of Principal Component Analysis, which minimizes the reconstruction error of the training documents used to compute a low-rank category transformation matrix. Such matrix transforms the original set of training documents from a given category to a new low-rank space and then optimally reconstructs them to the original space with a minimum reconstruction error. The proposed method, called Minimizer of the Reconstruction Error (mRE) classifier, uses this property, and extends and applies it to new unseen test documents. Several experiments on four multi-class datasets for text categorization are conducted in order to test the stable and generally better performance of the proposed approach in comparison with other popular classification methods.
