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This paper presents a practical and effective image compression system based on wavelet decomposition and contrast sensitive-SVR (support vector regression) for compressing still images. The kernel function in an SVR plays the central role of implicitly mapping the input vector (through an inner product) into a high-dimensional feature space. We study the different wavelet kernel for image compression...
A new approach for image compression has been presented, which transform the image data by block-based independent component analysis (ICA). Proposed method uses Hyvarinen and Oja's fast independent component analysis (FastICA) algorithm followed by arithmetic coding to perform compression. The number of required independent components are determined by measuring the quality parameters such as signal-to-noise...
Reduction of the image colors, which is also called color quantization (CQ), has been the focus of recent research interest. It is as an integral part of various digital image related areas such as compression, segmentation etc.. Neural networks play a significant role in either assisting conventional color quantization techniques or providing standalone solutions for color quantization. In the present...
In this paper, we presented a practical and effective image compression system based on Wavelet Support Vector Machine (WSVM) with Morlet wavelet kernel for compressing still images. The algorithm combines WSVM learning with discrete wavelet decomposition technique. Compression is achieved by approximating wavelet coefficients at each subband separately using WSVM regression. Results demonstrate in...
A modified forward-only counterpropagation neural network (MFO-CPN) for color image compression is proposed. It uses several higher-order distance measures for calculating winning node. It also incorporates nonlinear adjustment of learning rates in both the layers. Results with these distance functions are compared. Proposed modifications leads to improvement in the image quality and faster convergence...
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