6533b858fe1ef96bd12b620d
RESEARCH PRODUCT
Non-separable local polynomial regression cell-average multiresolution operators. Application to compression of images
Dionisio F. YáñezFrancesc Aràndigasubject
Polynomial regressionDecimationMathematical optimizationComputer Networks and CommunicationsApplied Mathematics020206 networking & telecommunicationsLinear prediction010103 numerical & computational mathematics02 engineering and technologyFunction (mathematics)01 natural sciencesStability (probability)Separable spaceOperator (computer programming)Control and Systems EngineeringCompression (functional analysis)Signal Processing0202 electrical engineering electronic engineering information engineering0101 mathematicsAlgorithmMathematicsdescription
Abstract Cell-average multiresolution Harten׳s algorithms have been satisfactorily used to compress data. These schemes are based on two operators: decimation and prediction. The accuracy of the method depends on the prediction operator. In order to design a precise function, local polynomial regression has been used in the last years. This paper is devoted to construct a family of non-separable two-dimensional linear prediction operators approximating the real values with this procedure. Some properties are proved as the order of the scheme and the stability. Some numerical experiments are performed comparing the new methods with the classical linear method.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-02-01 | Journal of the Franklin Institute |