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Image Compression Technique Using a Hierarchical Neural Network | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 9, Volume 3, Issue 2, 2006, Pages 99-112 PDF (589.3 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/csmj.2006.164055 | ||
| Authors | ||
| Rafid A. Khalil1; Mohammed C. Younis2 | ||
| 1College of engineering University of Mosul | ||
| 2Dep. Of Computer science Al-Hadbaa University College | ||
| Abstract | ||
| This paper present a Resilient Backpropagation (RBP) algorithm based on hierarchical neural network for image compression. The proposed technique includes steps to break down large images into smaller blocks for image compression/ decompression process. Furthermore, a Linear Backpropagation (LBP) algorithm is also used to train hierarchical neural network, and both training algorithms are compared. A number of experiments have been achieved, the results obtained, are the compression rate and Peak Signal to Noise Ratio of the compressed/ decompressed images which are presented in this paper. | ||
| Keywords | ||
| Image Compression; Resilient Backpropagation (RBP) algorithm; hierarchical neural network; Noise | ||
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