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JPEG, JPEG2000 and PBCS based Image Compression: An Experimental Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 58 - Number 10 |
| Year of Publication: 2012 |
| Authors: Jayavrinda Vrindavanam, Saravanan Chandran, Gautam K Mahanti, Vijayalakshmi K |
10.5120/9318-3550
|
Jayavrinda Vrindavanam, Saravanan Chandran, Gautam K Mahanti, Vijayalakshmi K . JPEG, JPEG2000 and PBCS based Image Compression: An Experimental Analysis. International Journal of Computer Applications. 58, 10 ( November 2012), 16-21. DOI=10.5120/9318-3550
Abstract
The paper attempts a comparison between Joint Picture Expert Group (JPEG), JPEG2000 and the novel method of position based coding scheme (PBCS) introduced by the authors, based on the output from different images. The results have proved that the proposed method is superior in terms of image compression ratio, PSNR and visual quality. After a review of various image compression standards and image compression coders, it is observed that there is a need to study the post-transformation matrix in a JPEG environment and accordingly, brought out a coding scheme based on the position of elements of the transform coefficients matrix after performing quantization. By identifying the unique elements and by reducing redundancies, the paper presents a novel method of coding called, PBCS. Thereafter, the results of JPEG, JPEG2000 with Huffman coding and PBCS are compared. The results show better compression ratio with higher PSNR and better image quality without quantization. The study can be considered as a logical extension of the image transformation matrix, applies statistical tools to achieve the novel coding scheme. The coding scheme can highly economise bandwidth without compromising picture quality; invariant to the existing compression standards, lossy as well as lossless compressions, which offers possibility for wide ranging applications.
References
- Gonzalez R. C, E. R. , and W. 2008 Digital Image processing, New Delhi: Pearson Pentice Hall, Third Edition, Low price edition, Pages(1-904).
- Jayavrinda Vrindavanam, Chandran. S. , and Mahanti, G. K . 2012. A survey of image compression methods. ;International Journal of computer application. Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET), March(pp. 12–17), Mumbai, India.
- Jayavrinda Vrindavanam, Chandran. S. , and Mahanti, G. K . 2012. Wavelet and JPEG based image compression: An experimental analysis. ; International Journal of computer application. Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET), March(pp. 36–42), Mumbai, India.
- Poorva, Jayavrinda, V. 2007. "Low Bit rate Movie Transmission: An exploratory Analysis. ; Proceedings of the Second International Conference on Industrial and Information Systems, Sri Lanka.
- Jayavrinda Vrindavanam. 2010. Video Compression for Movie Transmission: A Comparative Analysis. ; Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET), Feb (pp. 173–175),Mumbai, India. [Digital. ACM 978-1-60558-812-4].
- Sonja Grgic, M. M. 2001. Comparison of JPEG Image Coders. ; Proceedings of the 3rd International symposium on Video Processing and Multimedia Communications, June, (pp. 79-85). Zadar, Croatia.
- ISO/IEC JTCI/SC29/WGI N1646R. 2000. JPEG 2000 Part I Final Committee Draft Version 1. 0 March.
- Sonal, D. K. 2007. A study of various image compression techniques. COIT, RIMT-IET. Hisar.
- Oliver J. Malumbes, M. 2006. Low - Complexity Multiresolution Image Compression Using wavelet lower trees. IEEE Transactions on Circuits and Systems and Video Technology , Vo. No. 16, November.
- Cruz, Y. L. 2006. A Fast and Efficient Hybrid Fractal - Wavelet Image Coder. IEEE Transactions on Image Processing , January ,Vol 15, No. 1.
- Longji, W. E. -h. 2009. Joint Optimization of Run-Length coding, Huffman coding and Quantization Table with Complete Baseline JPEG Decoder Compatibility. IEEE Transactions on Image Processing , Vol 18, No. 1, January.
- Hasan, F. A. , Michael, T. 2009 . Spherical Coding Algorithm for Wavelet Image Compression. IEEE Transactions on Image Compression, Vol. 18, No. 5, pp. 1015-1024.
- Zadeh, P. B, A. S. A. ,and T. B. , and Soraghan, J. 2010. Multiresolution HVS and Statistically based Image Coding Scheme, Springer,( pp. 347-370) , Issue 49.
- Jeng, J. H. , Tseng, C. C. and Hsieh, J. G. 2009. Study on Huber Fractal Image Compression," IEEE Transactions on Image Processing, (pp. 995-1003), vol. 18, no. 5.
- Said, A. , Pearlman, W. A. 1996. A New,Fast, and Efficient Image Codec Based on Set Partitionaing in Hierarchical Trees. IEEE Transactions on Circuits and Systems for Video Technology, June, Volume 6, NO. 3.
- Shapiro, J. M. 1993. Embedded image coding using zerotrees of wavelets coefficients. IEEE Transactions on Signal Processing, December(pp. 3445-3462), Volume 41.
- Sulthana, N. S. , Mahesh C. 2010. Image Compression with Adaptive Arithmetic Coding. International Journal of Computer Applications, February, 1(1):31–34,
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