Image compression using principal component neural networks

Image and Vision Computing

Volumn 19, Issue 9-10, 2001, Pages 649-668

  Costa, S ^a   Fiori, S ^a  

^a UNIVERSITY OF PERUGIA   (Italy)

Author keywords

Artificial neural network; Karhunen Lo ve transform; Optimal bit allocation and coding; Principal component analysis; Still image compression

Indexed keywords

CORRELATION METHODS; IMAGE ANALYSIS; IMAGE CODING; LEARNING ALGORITHMS; MATHEMATICAL TRANSFORMATIONS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

KARHUNEN-LOEVE TRANSFORM; PRINCIPAL COMPONENT NEURAL NETWORKS;

IMAGE COMPRESSION;

EID: 0035419994     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0262-8856(01)00042-7     Document Type: Article

References (32)

1. Neural model for Karhunen-Loéve transform with application to adaptive image compression
2. Merging the transform step and the quantization step for Karhunen-Loève transform based image compression
3. Principal component extraction using recursive least squares learning
4. Adaptive learning algorithm for principal component analysis with partial data
5. A complex valued Hebbian learning algorithm
6. Blind separation of circularly distributed source signals by the neural extended APEX algorithm
7. Class of ψ-APEX PCA neural algorithms
8. A unified approach to laterally-connected neural nets
9. An experimental comparison of three PCA neural networks
10. Optimization criteria and nonlinear PCA neural networks
11. Stability of Oja's subspace rule
12. Learning of non-linear principal component subspace
13. Nonlinear generalizations of principal component learning algorithms
14. Representation and separation of signals using nonlinear PCA type learning
15. Generalizations of PCA, optimization problems, and neural networks
16. Nonlinear PCA type learning for source separation and independent component analysis
17. Adaptive principal component extraction (APEX) and applications
18. Orthogonal eigensubspace estimation using neural networks
19. A simplified neuron model as a principal component analyzer
20. Neural networks, principal components, and subspaces
21. Principal components, minor components, and linear neural networks
22. Beyond PCA: Statistical expansions by nonlinear neural networks
23. An experiment on comparing PCA and ICA in classical transform image coding
24. A self-organizing network for principal-component analysis
25. Optimal unsupervised learning in a single-layer linear feed-forward neural network
26. A bigradient optimization approach for robust PCA, MCA and source separation
27. Least mean square error reconstruction principle for self-organizing neural-nets