Removing Coding and Inter Pixel Redundancy in Image Compression

Anitha S; Kavitha J

doi:10.4108/eetsis.5073

Authors

Anitha S Govindammal Aditanar College for Women
Kavitha J Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

DOI:

https://doi.org/10.4108/eetsis.5073

Keywords:

Peak signal-to-noise ratio, Compression Ratio, Bits per pixel, singular value decomposition, bi-orthogonal

Abstract

The digital image plays an important role in today’s digital world. Storing and transmitting digital images efficiently is a challenging job. There are lots of techniques for reducing the size of digital pictures. This paper adapts the following method. The digital technique is separated into high and low resolutions. The low intensity and high intensity pixels single-handedly is dense and decompressed using three diverse algorithms to hit upon out the occurrence of low down intensity pixels in the picture. Totally six algorithms are experienced by means of benchmark images and the most excellent scheme is selected for concluding compression. A Comparison is made between the results obtained using these techniques and those obtained using JPEG 2000.

References

Jean-Loup, G, Nelson, M. The Data Compression Book. New York: Ausgabe , M & T Books; 1996.

Gonzalez, R.C. Digital image processing. Pearson education indi; 2009.

Eswara,R. B, Narayana, K . A lossless image compression using traditional and lifting based wavelets. Signal & Image Processing, 2012;Vol. (3): pp.213

Nagarajan,R, Romaniuk, G, Namuduri, K, R. A lossless image compression algorithm using variable block size segmentation. IEEE Transactions on Image Processing, 1995l;Vol. (4), pp. 1396-1406 .

Sun, W. A block-based MAP segmentation for image compressions. IEEE Transactions on Circuits and Systems for Video Technology, 1998; Vol.(8), pp.592-601.

Krishna, R, Ahuja, N. Lossless image compression with multiscale segmentation. IEEE Transactions on Image Processing, 2002; Vol. (11), pp.1228-1237.

Narendra, A. A transform for detection of multiscale image structure. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition IEEE. 1993;Vol. (1), pp.780-781.

Su, C, K, Hsin, K, C, Lin, S, K. Wavelet tree classification and hybrid coding for image compression. IEE Proceedings-Vision, Image and Signal Processing, 2005; Vol.(6), pp.752-756.

Ding, G. Distributed source coding theorem based region of interest image compression method. Electronics Letters, 2005; Vol. (4), pp. 1215-1217.

Anitha, S. 2D image compression technique-A survey. International Journal of Scientific & Engineering Research, V2011; Vol.(2), pp. 1-6.

Ricardo, L. Processing JPEG-compressed images and documents. IEEE Transactions on Image Processing, 1998;Vol.(7), pp.1661-1672.

Harby, A, A, Behery.G. M. Qualitative image compression algorithm relying on quadtree. Mansoura University, Faculty of Science, Math. Dept., ICGST-GVIP,2008; Vol.(8), pp. 2-3.

Hideo, K. 8 X 8 digital smart pixel array. Optics in Computing 2000; Vol.(4089), pp. 715-720.

Kuan, C. Block image retrieval based on a compressed linear quadtree. Image and Vision Computing, 2004; Vol. (22), pp. 391-397.

Paul, S, Li, X, Han, B.: A hybrid quantization scheme for image compression. Image and Vision Computing,2004; Vol. (22), pp.203-213.

CCITT, Recommendation H.261 : video codec for Audio visual service at p x 64 kbits/s,mar.1990 Draft Revision

ISO – IEC JT c1/SC2/WG11, Committee Draft of standard ISO 11/72, coding of moving pictures and associated audio, MPEG90/176 rev.2 ed., Dec.1990.Draft.

ISO-IEC JTC1/SC2/WG11, test model 2 for MPEG 2,MPEG 92/245 ed., July 1992.

Pennebaker, W, B , Mitchell, J, L. JPEG: Still image data compression standard. Springer Science & Business Media 1992.