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Vol.1 , No. 5, Publication Date: Jan. 10, 2015, Page: 75-85
 and Compression Ratio (CR) were used as performance metrics. PSNR was used as a measure of the resultant image quality after compression and the Compression Ratio was used as a measure of the degree of compression achievable. A trade-off was made between the achievable compression ratio and the realizable image quality which is a function of the achievable PSNR in the overall compression process. The overall performance of the proposed compression algorithm achieved an improvement in terms of compression ratio of 20:1 over the existing compression algorithms for biometric applications which achieved a compression ratio of 15:1. The improvement was largely due to the new approach employed in this research work.&picture=http://www.aascit.org/aascit/decorators/img/journal/901.jpg)
| [1] | Emmanuel B. S., Department of Electrical and Computer Engineering, Ahmadu Bello University, Zaria, Kaduna State. |
| [2] | Mu’azu M. B., Department of Electrical and Computer Engineering, Ahmadu Bello University, Zaria, Kaduna State. |
| [3] | Sani S. M., Department of Electrical and Computer Engineering, Ahmadu Bello University, Zaria, Kaduna State. |
| [4] | Garba S., Department of Electrical and Computer Engineering, Ahmadu Bello University, Zaria, Kaduna State. |
The implementation of the proposed biometric fingerprint image compression algorithm involved three stages, namely the transformation of biometric fingerprint image; non-uniform quantization of transformed image and the entropy coding which is the final stage. In order to determine the overall performance of the algorithm, Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR) were used as performance metrics. PSNR was used as a measure of the resultant image quality after compression and the Compression Ratio was used as a measure of the degree of compression achievable. A trade-off was made between the achievable compression ratio and the realizable image quality which is a function of the achievable PSNR in the overall compression process. The overall performance of the proposed compression algorithm achieved an improvement in terms of compression ratio of 20:1 over the existing compression algorithms for biometric applications which achieved a compression ratio of 15:1. The improvement was largely due to the new approach employed in this research work.
Keywords
Wavelet Transform, Compression, Biometric, Fingerprint, Quantization, Entropy Coding
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