Abstract

Wavelet transform technique has been used for image compression targeting high visual quality reconstructed images even with high compression ratio. A visual quality measure such as Picture Quality Scale (PQS), which correlates well with the subjective Mean Opinion Score (MOS) may be employed on the compressed image for the quantizer to select the optimum dynamic threshold. The use of optimum threshold permits the removal of redundant information, thus leading to better compression performance with acceptable picture quality. The Results obtained with the proposed approach of threshold selection is compared with the existing technique and the performance and it is found to be better in all of the cases of images or wavelets.

References

• CUI Huimin, ZHAO Ruimei, HOU Yanli, Improved Threshold Denoising Method Based on Wavelet Transform, Elsevier, Physics Procedia 33 ( 2012 ) 1354 – 1359,2012.
• G. Andria,F. Attivissimo,G. Cavone,N. Giaquinto,A. M. L. Lanzolla Linear filtering of 2D wavelet coefficients for denoising ultrasound medical images Elsevier, SciVerse Science Direct, Measurement 45(2012)1792– 1800,2012.
• D. L. Donoho, De-Noising by Soft Threshold, IEEE Trans. Info. Theory 43,pp. 933-936, 1993.
• D. L. Donoho and I. M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, Journal of American Statistical Assoc. , Vol. 90, no. 432, pp. 1200-1224, Dec. 1995.
• S. Grace Chang, Bin Yu and M. Vattereli, Adaptive Wavelet Threshold for Image Denoising and Compression, IEEE Trans. for Multiple Noisy Image Copies, IEEE Trans. Image Processing, vol. 9, pp. 1631- 1635, Sept. 2000.
• S. Grace Chang, Bin Yu and M. Vattereli, Wavelet Threshold for Multiple Noisy Image Copies, IEEE Trans. Image Processing, vol. 9, pp. 1631- 1635, Sept. 2000.
• S. Grace Chang, Bin Yu and M. Vattereli, Spatially Adaptive Wavelet Threshold with Context Modeling for Image Denoising,, IEEE Trans. Image Processing, vol. 9, pp. 1522-1530, Sept. 2000.
• Daubechies, Ten Lectures on Wavelets, Vol. 61 of Proc. CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia, PA: SIAM, 1992.
• Fei Xiaoa, Yungang Zhanga "A Comparative Study on Thresholding Methods in Waveletbased Image Denoising" Elsevier, Procedia Engineering 15 (2011) 3998 – 4003,2011.
• M. L. Hilton and R. T. Ogden Data Analytic Wavelet Threshold Selection in 2-D Signal Denoising" IEEE Transactions on signal processing, vol. 45, no. 2, february 1997.
• Weszka, J. S. ; Nagel, R. N. ; Rosenfeld, Threshold Selection Technique "Computers,IEEE Transactions on ,Volume C-23, Issue 12, Dec. 1974 Page(s):1322 - 1326
• Marc Antonini, Michel Barlaud, "Image Coding Using Wavelet Transform" IEEE Transactions on Image Processing, Vol. 1, No. 2, April 1992. pp. 205-220
• S. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation", IEEE Transactions Pattern Analysis Machine Intelligence, Vol II pp. 674-693, 1989.
• Miyahara, M. ,Kotani, K. , Algazi, V. , "Objective picture quality scale (PQS) for image coding" IEEE Transactions on Communications, Vol. 46 , Issue: 9 ,Sep 1998, pp. 1215-1226.
• N. W. Lewis and J. A. Alnatt "Subjective quality of television pictures with multiple impairments" Electron, Lett. ,vol. ,1,pp. 187-188,July
• Method for the subjective assessment of the quality of television pictures,CCIR Rec. 500-2,1982.
• "Advanced methods for the evaluation of television picture quality" in proc. , Mosiac workshop, Eindhoven,The Netherlands, 1995.
• Makoto Miyahara, Kazunori Kotani, and V. Ralph Algazi. Objective picture quality Scale(PQS) for image coding IEEE Ttransactions on communications, vol. 46, no. 9, september 1998.