6533b873fe1ef96bd12d4e72
RESEARCH PRODUCT
Perceptual adaptive insensitivity for support vector machine image coding.
Juan Manuel GutiérrezGabriel Gómez-pérezGustau Camps-vallsJesús Malosubject
Computer Networks and CommunicationsImage processingPattern Recognition AutomatedArtificial IntelligenceDistortionImage Interpretation Computer-AssistedDiscrete cosine transformComputer SimulationMathematicsModels StatisticalArtificial neural networkbusiness.industryPattern recognitionSignal Processing Computer-AssistedGeneral MedicineData CompressionComputer Science ApplicationsSupport vector machineFrequency domainVisual PerceptionA priori and a posterioriArtificial intelligencebusinessSoftwareAlgorithmsImage compressiondescription
Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant epsilon-insensitivity zone by Robinson and Kecman. 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 discrete cosine transform (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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2005-12-14 | IEEE transactions on neural networks |