Image Compression using Radial Basis Function Networks
نویسندگان
چکیده
In this paper it is proposed an image compression method based on the idea of fitting a set of Neural Networks (NNs) outputs to the image surface, which is a three-dimensional surface where the pixel values are considered as heights (z-values) defined on the x–y ground plane. An image is divided into subimages (blocks) using a quad tree, according to the complexity of the image surface. Individual block surfaces are fitted using a radial-basis function network (RBFN), and the parameters of the RBFN are stored as image representation in a compressed form. Key-Words: Image compression, Neural Networks, Radial Basis Function Network, Gaussian Approximation
منابع مشابه
Wavelet Networks Approach for Image Compression
In this paper, we present a direct solution method based on wavelet networks for image compression. Wavelet networks are a combination of radial basis function (RBF) networks and wavelet decomposition, where radial basis functions were replaced by wavelets. The results show that the wavelet networks approach succeeded to improve high performances in terms of compression ratio and reconstruction...
متن کاملCombining support vector machine learning with the discrete cosine transform in image compression
We present an algorithm for the application of support vector machine (SVM) learning to image compression. The algorithm combines SVMs with the discrete cosine transform (DCT). Unlike a classic radial basis function networks or multilayer perceptrons that require the topology of the network to be defined before training, an SVM selects the minimum number of training points, called support vecto...
متن کاملAn approach to improve Image Compression using Radial Basis Function Neural Network with Cumulative Distribution Function
Image compression is a key technology in the development of various multi-media computer services and telecommunication applications such as video conferencing, interactive education and numerous other areas. Image compression techniques aim at removing (or minimizing) redundancy in data, yet maintains acceptable image reconstruction. In general the images used for compression are of different ...
متن کاملExploitation of Sparse Properties of Support Vector Machines in Image Compression
In this paper we present a novel algorithm exploiting sparse pmperties of Support Vector Machines (SVM) with application to image compression. The algorithm combines SVM learning with the discrete cosine transform (DCT). Unlike a classic radial hasis function (RBF) networks or multilayer perceptrons that require the topology of the network to be defined before training, an SVM selects the minim...
متن کاملTuning Shape Parameter of Radial Basis Functions in Zooming Images using Genetic Algorithm
Image zooming is one of the current issues of image processing where maintaining the quality and structure of the zoomed image is important. To zoom an image, it is necessary that the extra pixels be placed in the data of the image. Adding the data to the image must be consistent with the texture in the image and not to create artificial blocks. In this study, the required pixels are estimated ...
متن کامل