JOURNAL ARTICLE

Application of Hybrid Back Propagation Neural Network in Image Compression

Abstract

A new hybrid neural network is showed in this paper for image compression, in which the hybrid genetic algorithm and BP algorithm approach are used to train the weight vector. The essence of the hybrid neural network in this paper is a feed-forward artificial neural network. It uses the hybrid intelligent learning algorithm for training. The advantage of genetic algorithm is the parallel search and high search efficiency. So its convergent speed and precision are improved greatly. The results of this method show high compression ratio, high ratio of signal vs. noise, low errors of coding, high decoding speed and fine resuming effect on subject.

Keywords:
Computer science Artificial neural network Decoding methods Backpropagation Image compression Data compression Compression ratio Artificial intelligence Coding (social sciences) Peak signal-to-noise ratio Genetic algorithm Hybrid neural network Feedforward neural network Hybrid algorithm (constraint satisfaction) Time delay neural network Deep learning Algorithm Pattern recognition (psychology) Image (mathematics) Image processing Machine learning Mathematics Engineering

Metrics

6
Cited By
0.00
FWCI (Field Weighted Citation Impact)
6
Refs
0.13
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Neural Networks and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Algorithms and Applications
Physical Sciences →  Engineering →  Control and Systems Engineering
Advanced Sensor and Control Systems
Physical Sciences →  Engineering →  Control and Systems Engineering

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