Comparison of some Algorithms in Image Compression Application
AL-Rafidain Journal of Computer Sciences and Mathematics,
2004, Volume 1, Issue 2, Pages 219-231
AbstractToday 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.
- Article View: 36
- PDF Download: 46