Hybrid coding for image compression
Keywords:
JPEG 2000, Lossless, Lossy compression, Discrete Cosine Transform, Fast Fourier transform, VBSAbstract
Image compression take a important part in digital world. Storing and transmitting digital image with high quality is a complex task. There are many methods for compressing digital images. In this paper, the following method is adapted. The digital image is divided into low and high intensity images. Discrete Cosine Transform (DCT) technique is applied to high intensity part of the image and fast Fourier transform (FFT) method is applied for low intensity pixels. The proposed method is tested with benchmark images and the results are compared with JPEG 2000 (Joint Photographic Experts Group 2000). It provides better results than JPEG 2000.
References
[1] M. Nelson and J. L Gaily, The data compression book, 2nd ed. New York: M&T books, 1996.
[2] R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Reading, MA: Addison-Welsley, 1992.
[3] Dr. B. Eswara Reddy and K Venkata Narayana “A Lossless Image Compression Using Traditional and Lifting Based Wavelets”, Signal & Image Processing: An International Journal (SIPIJ) Vol.3, No.2, and April 2012.
[4] N. Ranganathan, Steve G. Romaniuk, and Kameswara Rao Namuduri," A Lossless Image Compression Algorithm Using Variable Block Size Segmentation", IEEE Trans. Image Process., vol.14,no.10, pp.1396-1405, Oct.1995.
[5] Chee Sun Won, "A Block-Based MAP Segmentation for Image Compressions", IEEE Trans.on Circuits and systems for video technology, vol.8,no. 5,pp.592-601, September. 1998.
[6] Krishna Ratakonda, and Narendra Ahuja," Lossless Image Compression With Multiscale Segmentation", IEEE Trans.Image Processing,vol.11,no.11, pp.1228-1237, Nov.2002.
[7] N. Ahuja, “A transform for the detection of multiscale image structure,” IEEE Trans. Pattern Anal. Machine Intell., vol. 18, pp. 1211–1235, Dec.1996.
[8] C.-K. Su, H.-C. Hsin and S.-F. Lin, " Wavelet tree classification and hybrid coding for image compression" IEE Proceedings 2005.
[9] G. Ding, F. Yang, Q. Dai and W. Xu , " Distributed source coding theorem based region of interest image compression method" IEEE Electronics Letters, vol.41, no.22, Oct.2005.
[10] A. Bradley and F. Stienford,”JPEG2000 and region of interest coding”,in proc. Int. conf.DICTA2002, Melbourne, Australia, Jan2002 .
[11] Ricardo L. de Queiroz “Processing JPEG-Compressed Images and Documents”, IEEE Transactions on Image Processing, Vol. 7, No. 12, Pp1661-1667december 1998.
[12] A.A. El-Harby and G.M. Behery," Qualitative Image Compression Algorithm Relying on Quadtree" , ICGST-GVIP, ISSN 1687-398X, Volume (8), Issue (III), October 2008.
[13] H. Kawai, A. BABA, Y. Takeuchi, T.Komuro, and M. Ishikawa, "8x8 Digital Smart Pixel Array", In Optics in Computing,R.A.Lessard, T.Galstian, Ed., SPIE 4089, 2000. [14] Yung-Kuan Chan, Chin-Chen Chang, "Bloch image retrieval based on a compressed linear quadtree", Image and Vision Computing, 22(5): 391-397, 2004.
[15] Paul Shelley, Xiaobo Li, Bin Han,“A hybrid quantization scheme for image compression”,University of Alberta,2003
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
