Comparison of some Algorithms in Image Compression Application
Today there are a number of algorithms developed in the framework of international committees that allow still image compression. In this paper, the area of Vector Quantization (VQ) neural network with the Self-Organizing Feature Map (SOFM) has been compared with the ordinary vector Quantization tec...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Mosul University
2004-12-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.mosuljournals.com/article_164121_8ce6991731c4292aa0ff594a6f01a001.pdf |
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| Summary: | Today there are a number of algorithms developed in the framework of international committees that allow still image compression. In this paper, the area of Vector Quantization (VQ) neural network with the Self-Organizing Feature Map (SOFM) has been compared with the ordinary vector Quantization technique Linde-Buzo-Gray (LBG) in image compression. The results were compared with the Back Propagation Neural Network BPNN which was employed to design a code book of an image to be compressed using VQ method. Results show that the neural technique gives a performance that is very close to optimal. The BPNN scheme not only has the advantage of the SOFM - VQ scheme but also improves the coded image quality. Experimental results are given and comparisons are made using the BPNN coding scheme and some other coding techniques. In the experiments, the BPNN coding scheme achieves the better visual quality about edge region and the best peak signal-to-noise ratio PSNR performance at nearly the same bit rate.<strong> </strong> |
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| ISSN: | 1815-4816 2311-7990 |