Joint sparsity-based optimization of a set of orthonormal 2-D separable block transforms

We propose an iterative method for the optimization of a set of 2-D separable transforms for a given training data set. The method outputs orthonormal transforms, each one being optimal for a subset of the data with respect to a sparsity-based objective function. The vertical and horizontal directio...

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Bibliographic Details
Published in2009 16th IEEE International Conference on Image Processing (ICIP) pp. 9 - 12
Main Authors Sole, J., Peng Yin, Yunfei Zheng, Gomila, C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Coding
Cost function
Discrete cosine transforms
Discrete transforms
Frequency
Image reconstruction
Karhunen-Loeve transforms
Optimization methods
Quantization
Separable Transforms
Sparsity
Training data
Transform optimization
Video coding
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Summary:We propose an iterative method for the optimization of a set of 2-D separable transforms for a given training data set. The method outputs orthonormal transforms, each one being optimal for a subset of the data with respect to a sparsity-based objective function. The vertical and horizontal directions of the transform may be different, thus allowing directional-adapted transforms (in contrast to the usual DCT). Additionally, we relate the reconstruction error and the sparsity cost terms through the quantization step. To prove the validity of our approach, experimental results concerning coding applications are provided.
ISBN:9781424456536
1424456533
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2009.5413929