Ultrafast and Efficient Scalable Image Compression Algorithm

Ali Kadhim Jaber Al-Janabi


Wavelet-based image compression algorithms have good performance and produce a rate scalable bitstream that can be decoded efficiently at several bit rates. Unfortunately, the discrete wavelet transform (DWT) has relatively high computational complexity. On the other hand, the discrete cosine transform (DCT) has low complexity and excellent compaction properties. Unfortunately, it is non-local, which necessitates implementing it as a block-based transform leading to the well-known blocking artifacts at the edges of the DCT blocks. This paper proposes a very fast and rate scalable algorithm that exploits the low complexity of DCT and the low complexity of the set partitioning technique used by the wavelet-based algorithms. Like JPEG, the proposed algorithm first transforms the image using block-based DCT. Then, it rearranges the DCT coefficients into a wavelet-like structure. Finally, the rearranged image is coded using a modified version of the SPECK algorithm, which is one of the best well-known wavelet-based algorithms. The modified SPECK consumes slightly less computer memory, has slightly lower complexity and slightly better performance than the original SPECK. The experimental results demonstrated that the proposed algorithm has competitive performance and high processing speed. Consequently, it has the best performance to complexity ratio among all the current rate scalable algorithms.

Full Text:



Yun, Q.S. & Huifang, S., Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms, and standards, 2nd ed., CRC Press, Massachusetts, USA, 2008.

Salomon, D., Data Compression: the Complete Reference, 3rd ed., Springer, New York, USA, 2004.

Feig, E., A Fast Scaled DCT Algorithm, in Proc. SPIE Image Processing Algorithms and Techniques, Santa Clara, USA, 1244, pp. 2-13, Feb. 1990.

Yen, W. & Chen, Y., DCT-Based Image Compression with Efficient Enhancement Filter, 23rd Inter. Technical Conference on Circuits/Systems, Computers and Communications, Shimonoseki City, Japan, pp. 1225-1228, 2008.

Rabbani, M. & Joshi, R., An Overview of the JPEG 2000 Still Image Compression Standard, Signal Processing: Image Communication, 17(1), pp. 3-48, 2002.

Al-Janabi, A.K., Highly Scalable Single List Set Partitioning in Hierarchical Trees Image Compression, IOSR Journal of Electronics and Communication Engineering, 9(1), pp. 36-47, 2014. DOI: 10.9790/2834-09133647.

Said, A. & Pearlman, W.A., A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees, IEEE Trans. on Circuits & Systems for Video Technology, 6(3), pp. 243-250, 1996.

Pearlman, W.A, Islam, A., Nagaraj, N. & Said, A., Efficient, Low Complexity Image Coding with a Set-Partitioning Embedded Block Coder, IEEE Trans. on Circuits &Systems for Video Technology, 14(11), pp. 1219-1235, Nov. 2004.

Song, H.S. & Cho, N.I., DCT-Based Embedded Image Compression with a New Coefficient Sorting Method, IEEE Signal Processing Letters, 16(5), pp. 410-413, 2009.

Pearlman, W.A., Trends of Tree-Based, Set-Partitioning Compression Techniques in Still and Moving Image Systems, Proceedings Picture Coding Symposium (PCS-2001), Seoul, Korea, 25-27, pp. 1-8, April, 2001.

Berman, A.M., Data Structures via C++: Objects by Evolution, 1st edition, Oxford University Press, New York, USA, 1997.

Tu, C., & Tran, T.D., Context-Based Entropy Coding of Block Transform Coefficients for Image Compression, IEEE Trans. Image Processing, 11(11), pp. 1271-1283, 2002.

Panggabean, M., Maciej W., Harald, Ø. & Leif, A.R., Ultrafast Scalable Embedded DCT Image Coding for Tele-immersive Delay-Sensitive Collaboration, Inter. Journal of Advanced Computer Science and Applications, 4(12), pp. 202-211, 2013.

Wheeler, F.W. & Pearlman, W.A., Combined Spatial and Subband Block Coding of Images, IEEE Int. Conf. on Image Processing (ICIP2001), Vancouver, BC, Canada, Sept., 2000.

Xiong, Z., Ramchandran, K., Orchard, M.T. & Zhang, Y.Q., A Comparative Study of DCT and Wavelet-based Image Coding, IEEE Trans. On Circuits & Systems for Video Technology, 9(5), pp. 692-695, 1999.

http://www.cipr.rpi.edu/research/SPIHT (Visited at January 2015).

Malvar, H., Progressive Wavelet Coding of Images, IEEE Data Compression Conference, Salt Lake City, UT, pp. 336-343, March 1999.

DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2015.9.3.2


  • There are currently no refbacks.

Contact Information:

ITB Journal Publisher, LPPM – ITB, 

Center for Research and Community Services (CRCS) Building Floor 7th, 
Jl. Ganesha No. 10 Bandung 40132, Indonesia,

Tel. +62-22-86010080,

Fax.: +62-22-86010051;

e-mail: jictra@lppm.itb.ac.id.