The paper attempts a comparison between Huffmancoding and Position Based Coding Scheme introduced by the authors. 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 JPEGenvironment 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 presented a novel method of coding called, PBCS. Thereafter, the results of Joint Picture Expert Group (JPEG) with Huffman coding and PBCS are compared. The results showbetter compression ratio with higher PSNR and better image quality with quantization. The study can be considered as a logical extension of the discrete cosine transformation matrix of an image,andapplies statistical tools to achieve the novel coding scheme. The coding scheme can highly economisethe bandwidth without compromising on picture quality; invariant to the existing compression standards and lossy as well as lossless compressions which offers possibility for wide ranging applications.

1. Gonzalez R. C, E. R. , and W. 2008 Digital Image processing, New Delhi: Pearson Pentice Hall, Third Edition, Low price edition,1-904.
2. Jayavrinda V. , Chandran. S. , and Mahanti, G. K . 2012. Asurvey of image compression methods. ;International Journal of computer application. Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET),(March. 2012), 12–17, Mumbai, India.
3. ISO/IEC JTCI/SC29/WGI N1646R. 2000. JPEG 2000 Part I Final Committee Draft Version 1. 0 March.
4. Sonja Grgic, M. M. 2001. Comparison of JPEG Image Coders. ; In Proceedings of the 3rd International symposium on Video Processing and Multimedia Communications, (June. 2001),79-85. Zadar, Croatia.
5. Grgic, S ,K. K. and Grgic, M. 1999. Image Compression using Wavelets. IEEE International Symposium on Industrial Electronics, ISIE'99, Bled, Solvania.
6. Sonal, D. K. 2007. A study of various image compression techniques. COIT, RIMT-IET. Hisar.
7. Querioz, Z. , Fan, Ricardo,L. 2003. Identificaiton of Bitmap Compression History: JPEG Detection and Quantizer Estimation. In IEEE Transactions on Image Processing,(Feb. 2003), Vol. 12, No. 2.
8. Longji, W. E. -h. 2009. Joint Optimization of Run-Length coding,Huffman coding and Quantization Table with Complete Baseline JPEG DecoderCompatibility. IEEE Transactions on Image Processing , (Jan. 2009),Vol18, No. 1
9. Jeng, J. H. , Tseng, C. C. , and Hsieh, J. G. 2009. Study on Huber Fractal Image Compression. In IEEE Transactions on Image Processing,vol. 18, no. 5, 995-1003.
10. ISO/IEC. 1991. Digital Compression and Coding for Continuous Tone Still Images.

JPEG image compression wavelet DCT PBCS