Image coding using wavelet transforms and vector quantization with error correction

Robotica ◽  
1999 ◽  
Vol 17 (2) ◽  
pp. 219-227
Author(s):  
H. Zenkouar ◽  
A. Nachit
Keyword(s):  
Image Compression ◽  
Visual System ◽  
Error Correction ◽  
Vector Quantization ◽  
Image Coding ◽  
Wavelet Transforms ◽  
Self Organizing Maps ◽  
Frequency Domains ◽  
Self Organizing

Image compression is essential for applications such as transmission of databases, etc. In this paper, we propose a new scheme for image compression combining recursive wavelet transforms with vector quantization. This method is based on the Kohonen Self-Organizing Maps (SOM) which take into account features of a visual system in both space and frequency domains.

Self Organizing Maps, Vector Quantization, and Fractal Image Coding

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 201-214
Author(s):  
Paolo Carlini
Keyword(s):  
Vector Quantization ◽  
Image Coding ◽  
Clustering Algorithms ◽  
Common Point ◽  
Point Of View ◽  
Self Organizing Maps ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Block Based ◽  
Self Organizing

This paper reviews the properties of a popular kind of neural network, called self organizing maps, then discusses their relevance for the field of lossy image coding. In particular, vector quantization (VQ) and fractal (block based) coding are studied from a common point of view, emphasizing their relationships. The latter is an exotic kind of (non-causal) predictive-VQ; clustering algorithms, directly useful in the VQ field, can be exploited to reduce its computational complexity.

Image Compression using self-Organizing Maps

2003 ◽  
Vol 43 (11) ◽  
pp. 1529-1543 ◽  
Author(s):  
C. Amerijckx ◽  
J.-D Legat ◽  
M Verleysen
Keyword(s):  
Image Compression ◽  
Self Organizing Maps ◽  
Self Organizing

Codebook Clustering by Self-Organizing Maps for Fractal Image Compression

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 27-38 ◽  
Author(s):  
Raouf Hamzaoui
Keyword(s):  
Image Compression ◽  
Clustering Algorithm ◽  
Not Given ◽  
Fractal Image Compression ◽  
Self Organizing Maps ◽  
Classification Schemes ◽  
Fast Encoding ◽  
Fractal Image ◽  
Traditional Classification ◽  
Self Organizing

A fast encoding scheme for fractal image compression is presented. The method uses a clustering algorithm based on Kohonen's self-organizing maps. Domain blocks are clustered, yielding a classification with a notion of distance which is not given in traditional classification schemes.

scholarly journals Prototype Generation Using Self-Organizing Maps for Informativeness-Based Classifier

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Leandro Juvêncio Moreira ◽  
Leandro A. Silva
Keyword(s):  
Vector Quantization ◽  
Similarity Measure ◽  
Nearest Neighbor ◽  
Optimal Choice ◽  
Border Region ◽  
Training Dataset ◽  
Self Organizing Maps ◽  
Nearest Neighbor Rule ◽  
Two Parameters ◽  
Self Organizing

The k nearest neighbor is one of the most important and simple procedures for data classification task. The kNN, as it is called, requires only two parameters: the number of k and a similarity measure. However, the algorithm has some weaknesses that make it impossible to be used in real problems. Since the algorithm has no model, an exhaustive comparison of the object in classification analysis and all training dataset is necessary. Another weakness is the optimal choice of k parameter when the object analyzed is in an overlap region. To mitigate theses negative aspects, in this work, a hybrid algorithm is proposed which uses the Self-Organizing Maps (SOM) artificial neural network and a classifier that uses similarity measure based on information. Since SOM has the properties of vector quantization, it is used as a Prototype Generation approach to select a reduced training dataset for the classification approach based on the nearest neighbor rule with informativeness measure, named iNN. The SOMiNN combination was exhaustively experimented and the results show that the proposed approach presents important accuracy in databases where the border region does not have the object classes well defined.

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