Image compression with neural networks – A survey

• Published in: Signal processing. Image communication Vol. 14; no. 9; pp. 737 - 760
• Main Author: Jiang, J.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 1999; Elsevier
• Subjects: Applied sciences; Coding, codes; Exact sciences and technology; Image compression and coding; Image processing; Information, signal and communications theory; Neural network; Signal and communications theory; Signal processing; Telecommunications and information theory; Neural network; Image compression and coding; Vector quantization; Image processing; Genetic algorithm; Coding; Data compression
• ISSN: 0923-5965; 1879-2677
• DOI: 10.1016/S0923-5965(98)00041-1

Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as neural networks and genetic algorithms are being developed to explore the future of image coding. Successful applications of neural networks to vector quantization have now become well established, and other aspects of neural network involvement in this area are stepping up to play significant roles in assisting with those traditional technologies. This paper presents an extensive survey on the development of neural networks for image compression which covers three categories: direct image compression by neural networks; neural network implementation of existing techniques, and neural network based technology which provide improvement over traditional algorithms.