Name
Abstract | ||
|---|---|---|
Abstract— Support Vector Machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant "-insensitivity zone by Robinson and Kecman [1]. However, according to the statistical properties of natural images and the properties of human perception, a constant insensitivity makes,sense in the spatial domain,but it is certainly not a good option in a frequency domain. In fact, in their approach, they made,a fixed low-pass assumption as the number,of DCT coefficients to be used in the training was limited . This paper extends the work of Robinson and Kecman by proposing the use of adaptive insensitivity SVMs [2] for image coding using an appropriate distortion criterion [3], [4] based on a simple visual cortex model. Training the SVM by using an accurate perception model avoids any a priori assumption and improves the rate-distortion performance of the original approach. Index Terms— Support Vector Machine, Adaptive Insensitivity, |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TNN.2005.857954 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
adaptive insensitivity svms,constant insensitivity,maximum perceptual error.,human perception,priori assumption,image coding,perceptual metric,fixed low-pass assumption,accurate perception model,support vector machine (svm),insensitivity zone,distortion criterion,perceptual adaptive insensitivity,inference mechanisms,statistical analysis,discrete cosine transform (dct),natural images,learning (artificial intelligence),discrete cosine transform coefficients,maximum perceptual error,perception model,support vector machine image,dct,discrete cosine transforms,support vector machine learning,support vector machine image coding,frequency domain,spatial domain,statistical property,adaptive insensitivity,visual cortex model,support vector machines,rate-distortion performance,index terms— support vector machine,image compression,natural image,sensitivity analysis,low pass,learning artificial intelligence,discrete cosine transform,support vector machine,indexing terms | Frequency domain,Pattern recognition,Computer science,A priori and a posteriori,Discrete cosine transform,Support vector machine,Image processing,Artificial intelligence,Artificial neural network,Distortion,Image compression,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 6 | 1045-9227 |
Citations | PageRank | References |
7 | 0.54 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Gómez-Pérez | 1 | 7 | 0.54 |
| Camps-Valls, G. | 2 | 441 | 29.69 |
| Juan Gutiérrez | 3 | 7 | 0.54 |
| J Malo | 4 | 195 | 32.92 |