Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Adaptive image processing: a computational intelligence perspective
Adaptive image processing: a computational intelligence perspective
The Transform and Data Compression Handbook
The Transform and Data Compression Handbook
Handbook of Neural Network Signal Processing
Handbook of Neural Network Signal Processing
Image Compression by Layered Quantum Neural Networks
Neural Processing Letters
Data Compression
JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures
JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures
Document and Image Compression (Signal Processing and Communications)
Document and Image Compression (Signal Processing and Communications)
HOSVD based image processing techniques
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
HOSVD based data representation and LPV model complexity reduction
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
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In this paper is offered a method for non-linear still image representation based on pyramidal decomposition with a neural network. This approach is developed by analogy with the hypothesis for the way humans do image recognition using consecutive approximations with increasing similarity. A hierarchical decomposition, named Inverse Difference Pyramid (IDP), is used for the image representation. The approximations in the consecutive decomposition layers are represented by the neurons in the hidden layers of the neural networks (NN). This approach ensures efficient description of the processed images and as a result -- a high compression ratio. This new way for image representation is suitable for various applications (efficient compression, multi-layer search in image databases, etc.).