Methods exploring the application of support vector machine learning (SVM) to still image compression are detailed in both the spatial and frequency domains. In particular the sparse properties of SVM learning are exploited in the compression algorithms. A classic radial basis function neural network requires that the topology of the network be defined before training. An SVM has the property that it will choose the minimum number of training points to use as centres of the Gaussian kernel functions. It is this property that is exploited as the basis for image compression algorithms presented in this book. Several novel algorithms are developed applying SVM learning to both directly model the colour surface and model transform coefficients after the surface has been transformed into the frequency domain. It is demonstrated that compression is more efficient in frequency space. In the frequency domain, results are superior to that of JPEG. For example, the quality of the industry standard ¿Lena¿ image compressed 63:1 for JPEG is slightly worse quality than the same image compressed 192:1 with the RKi-1 algorithm detailed in this book.
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Jonathan Robinson, PhD, MSc, DipSci, BSc: Studied physics and engineering at Auckland and Otago universities. He has worked as a scientist for the New Zealand military and has held various senior development and management roles in the IT industry. Principal of consultancy firm synap-tech.com, specialising in machine learning.
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Kartoniert / Broschiert. Condición: New. Methods exploring the application of support vectormachine learning (SVM) to still image compression aredetailed in both the spatial and frequency domains.In particular the sparse properties of SVM learningare exploited in the compre. Nº de ref. del artículo: 4957386
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Taschenbuch. Condición: Neu. Support Vector Machine Learning | Application to Compression of Digital Images | Jonathan Robinson | Taschenbuch | Kartoniert / Broschiert | Englisch | 2008 | VDM Verlag Dr. Müller | EAN 9783639100006 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. Nº de ref. del artículo: 101695329
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Methods exploring the application of support vectormachine learning (SVM) to still image compression aredetailed in both the spatial and frequency domains.In particular the sparse properties of SVM learningare exploited in the compression algorithms. Aclassic radial basis function neural network requiresthat the topology of the network be defined beforetraining. An SVM has the property that it will choosethe minimum number of training points to use ascentres of the Gaussian kernel functions. It is thisproperty that is exploited as the basis for imagecompression algorithms presented in this book.Several novel algorithms are developed applying SVMlearning to both directly model the colour surfaceand model transform coefficients after the surfacehas been transformed into the frequency domain. It isdemonstrated that compression is more efficient infrequency space.In the frequency domain, results are superior to thatof JPEG. For example, the quality of the industrystandard Lena image compressed 63:1 for JPEG isslightly worse quality than the same image compressed192:1 with the RKi-1 algorithm detailed in this book. Nº de ref. del artículo: 9783639100006
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