International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 3 | Issue 3 | Year 2012 | Article Id. IJCTT-V3I3P120 | DOI : https://doi.org/10.14445/22312803/IJCTT-V3I3P120

Context-Based Adaptive Variable Length Coding based Compression Scheme for Images


V. Subramanian, V. R. Radhika, V. Kalaipoonguzhali, M. Nageswari

Citation :

V. Subramanian, V. R. Radhika, V. Kalaipoonguzhali, M. Nageswari, "Context-Based Adaptive Variable Length Coding based Compression Scheme for Images," International Journal of Computer Trends and Technology (IJCTT), vol. 3, no. 3, pp. 436-439, 2012. Crossref, https://doi.org/10.14445/22312803/IJCTT-V3I3P120

Abstract

In order to achieve a high compression ratio, the Context-Based Adaptive Variable Length Coding (CBAVLC) standard has incorporated a large number of coding modes which must be evaluated during the coding process to determine the optimal rate-distortion trade-off. The coding gains of CBAVLC arise at the expense of significant coder complexity. One coder process that has been identified as having potential for achieving computation savings is the selection between skipping the coding of a macro block and coding of the macro block in one of the remaining coding modes. In low contrast images, a large percentage of macro blocks are “skipped”, that is, no coded data are transmitted for these macro blocks. By estimating and identifying macro blocks to be skipped during the coding process, significant savings in computation can be realized, since the coder then does not evaluate the rate-distortion costs of all candidate coding modes. The proposed scheme shows that this approach can result in a time savings of over 80% for low contrast images at a negligible decrease or, in certain cases, a slight increase in quality over a reference codec.

Keywords

Context-Based Adaptive Variable Length Coding (CBAVLC), Macro Blocks (MB), Low-contrast images.

References

[1] Xiwen OwenZhao, Zhihai HenryHe, “Lossless Image Compression Using Super-Spatial Structure Prediction”, IEEE Signal Processing Letters, vol. 17, no. 4, April 2010.
[2] Aaron T. Deever and Sheila S. Hemami, “Lossless Image Compression With Projection-Based and Adaptive Reversible Integer Wavelet Transforms”, IEEE Transactions on Image Processing, vol. 12, no. 5, May 2003.
[3] Nikolaos V. Boulgouris, Dimitrios Tzovaras, and Michael Gerassimos Strintzis, “Lossless Image Compression Based on OptimalPrediction, Adaptive Lifting, and Conditional Arithmetic Coding”, IEEE Transactions on Image Processing, vol. 10, NO. 1, Jan 2001.
[4] Xin Li and Michael T. Orchard, “Edge-Directed Prediction for Lossless Compression of Natural Images”, IEEE Transactions on Image Processing, vol. 10, NO. 6, Jun 2001.
[5] Jaemoon Kim, Jungsoo Kim and Chong-Min Kyung, “A Lossless Embedded Compression Algorithm for High Definition Video Coding”, 978-1-4244-4291 / 09 2009 IEEE, ICME 2009.
[6] Rene J. van der Vleuten, Richard P.Kleihorstt, Christian Hentschel,t, “Low-Complexity Scalable DCT Image Compression”, IEEE Transactions, 2000.
[7] K.Somasundaram, and S.Domnic, “Modified Vector Quantization Method for mage Compression”, Transactions on Engineering, Computing And Technology Vol 13 May 2006.
[8] Mohamed A. El-Sharkawy, Chstian A. White and Harry, “Subband Image Compression Using Wavelet Transform and Vector Quantization”, IEEE Transactions, 1997.
[9] Roger L. Claypoole, Jr.Geoffrey M. Davis, Wim Sweldens, “Nonlinear Wavelet Transforms for Image Coding via Lifting”, IEEE Transactions on Image Processing, vol. 12, NO. 12, Dec 2003.
[10] David Salomon, “Data Compression - Complete Reference”, Springer- Verlag New York, Incorporation, ISBN 0-387-40697-2.
[11] Eddie Batista de Lima Filho, Eduardo A. B. da Silva Murilo  Bresciani de Carvalho and Frederico Silva Pinagé, “Universal Image  Compression  Using  Multiscale    Recurrent  Patterns  With  Adaptive  Probability Model”, IEEE Transactions on Image Processing, vol. 17,  NO. 4, Apr 2008.
[12] Ingo Bauermann, and Eckehard Steinbach, “RDTC Optimized  Compression  of  Image-Based  Scene  Representations (Part  I): Modeling and Theoretical Analysis”, IEEE Transactions on Image Processing, vol. 17, NO. 5, May 2008.
[13] Roman Kazinnik, Shai Dekel, and Nira Dyn, “Low Bit-Rate Image  Coding  Using  Adaptive  Geometric  Piecewise  Polynomial Approximation”, IEEE Transactions on  Image Processing, vol. 16, no. 9, Sep 2007.
[14] Marta Mrak, Sonja Grgic, and Mislav Grgic, “Picture Quality  Measures  in  Image  Compression  Systems”,  EUROCON 2003, Ljubljana, Slovenia, 0-7803-7763-W03, IEEE 2003. 
[15] Alan C. Brooks, Xiaonan Zhao, Thrasyvoulos N. Pappas., “Structural Similarity Quality Metrics in a Coding Context: Exploring the Space of Realistic  Distortions”,  IEEE  Transactions  on  Image Processing, vol. 17, no. 8, pp. 1261-1273, Aug 2008.
[16] Hong, S. W. Bao, P., “Hybrid image compression model based  on subband coding and edge-preserving regularization”, Vision Image  and Signal Processing, IEEE Proceedings, Volume: 147, Issue: 1, 16- 22, Feb 2000. [17] Zhe-Ming Lu,   Hui Pei, “Hybrid Image Compression Scheme Based on PVQ and DCTVQ”, IEICE - Transactions on Information and Systems archive, Vol E88-D,  Issue 10, October 2006.
[18] Y.Jacob  Vetha  Raj,  M.Mohamed  Sathik  and  K.Senthamarai Kanna,  “Hybrid Image Compression by Blurring Background and NonEdges. The International Journal on Multimedia and its applications, Vol. 2, No.1, pp 32-41, February 2010.
[19] Willian K. Pratt, “Digital Image Processing”, John Wiley & Sons, Inc,  ISBN 9-814-12620-9.
[20] Jundi Ding, Runing Ma and Songcan Chen, “A  Scale-Based Connected Coherence Tree Algorithm for Image Segmentation”, IEEE Transactions on Image Processing, vol. 17, NO. 2, Feb 2008.
[21] Kyungsuk (Peter) Pyun, Johan Lim, Chee Sun Won and Robert M. Gray, “Image Segmentation Using Hidden Markov Gauss Mixture Models”, IEEE Transactions on Image Processing, Vol. 16, No. 7, July 2007.