Journal ArticleDOI
A New Compression Technique Using an Artificial Neural Network
TLDR
The proposed technique includes steps to break down large images into smaller windows and eliminate redundant information and employs a neural network trained by a non-iterative, direct solution method for image compression.Abstract:
In this paper, we present a direct solution method based neural network for image compression. The proposed technique includes steps to break down large images into smaller windows and eliminate redundant information. Furthermore, the technique employs a neural network trained by a non-iterative, direct solution method. An error backpropagation algorithm is also used to train the neural network, and both training algorithms are compared. The proposed technique has been implemented in C on the SP2 Supercomputer. A number of experiments have been conducted. The results obtained, such as compression ratio and transfer time of the compressed images are presented in this paper.read more
Citations
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Proceedings ArticleDOI
Methodology of Optimization of Identification of the Contour and Brightness-Color Picture of Images of Micro-Objects
TL;DR: In this paper, a methodology has been developed for optimizing the identification and processing of images of pollen grains based on the use of mechanisms for extracting texture, specific characteristics, and geometric features of micro-objects.
Proceedings ArticleDOI
Optimization of Micro-Object Identification by Correcting Distorted Image Points
TL;DR: In this article , a methodology for optimizing the identification, recognition and classification of micro-objects has been implemented using dynamic models for transforming the original image, synthesizing mechanisms for extracting redundant information structures and using histological, morphological, and texture characteristics of images.
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A Technique for Image Compression by using GSOM Algorithm
TL;DR: This research work a technique is proposed for image compression which is based on Neural Network, and may be compression up to 90% of the source files.
Journal ArticleDOI
Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables
TL;DR: In this paper , a generalized model for optimizing the identification of RTS based on the use of neural networks, neuro-fuzzy networks of dynamic models, as well as fuzzy logic algorithms is implemented.
Proceedings ArticleDOI
Optimization of Micro-object Identification Based on the Mellin Transform and the Use of Parallel Computing
TL;DR: In this article , the optimal identification of non-stationary objects based on the use of neural networks has been developed, where models and algorithms for detection, extraction of hidden relationships, useful properties and patterns in data, formation of a database and knowledge bases are proposed.
References
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Book
Data Compression Book
TL;DR: In this article, the authors present a guide to data compression techniques, including Shannon-Fano and Huffman coding techniques, lossy compression, JPEG compression algorithm, and fractal compression.
Book
The Data Compression Book
TL;DR: In this paper, the authors present a guide to data compression techniques, including Shannon-Fano and Huffman coding techniques, lossy compression, JPEG compression algorithm, and fractal compression.
Journal ArticleDOI
Dynamic Node Creation in Backpropagation Networks
TL;DR: A new method called Dynamic Node Creation (DNC) which automatically grows BP networks until the target problem is solved, and yielded a solution for every problem tried.
Journal Article
Data compression
D.R. Helman,Glen G. Langdon +1 more
TL;DR: The applications of digital data compression and the major components of compression systems are described and data modeling is discussed, and the role of entropy and data statistics is examined.
Journal ArticleDOI
Neural network approaches to image compression
R.D. Dony,Simon Haykin +1 more
TL;DR: This paper presents a tutor a overview of neural networks as signal processing tools for image compression due to their massively parallel and distributed architecture.