This chapter presents a lossless color image compression scheme using the degree-k zerotree coding technique. From studies carried out on the degree-k zerotree coding, it has been found that at lower bit-rates, a higher degree zerotree coding gives a better coding performance whereas at higher bit-rates, coding with a lower degree zerotree is more efficient. Hence, the degree of zerotree tested is tuned in each encoding pass in the proposed Tuned Degree-K Zerotree Wavelet (TDKZW) coding to obtain an optimal compression performance. Since the TDKZW coder uses the set-partitioning approach similar to the Set-Partitioning in Hierarchical Trees (SPIHT) coder, it allows embedded coding and also enables progressive transmission to take place. In addition, a new spatial orientation tree (SOT) structure for color coding is also proposed here for low memory implementation of the TDKZW coder (LM-TDKZW). Simulation results on standard test images show that the proposed TDKZW coder gives a better lossless color image compression performance than the SPIHT coder. The results also show that the proposed LM-TDKZW coder not only requires as little as 6.25% of the memory needed by the SPIHT coder, it is able to achieve an almost equivalent lossless compression performance as the SPIHT coder.
Color image compression Degree-k zerotree coding Lossless compression Spatial orientation tree structure Wavelet-based image coding
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Christopoulos, C., Skodras, A., & Ebrahimi, T. (2000). The JPEG still image coding system: an overview. IEEE Transactions on Consumer Electronics, 46, 1103–1127.CrossRefGoogle Scholar
Shapiro, J.M. (1993). Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing, 41(12), 3445–3462.zbMATHCrossRefGoogle Scholar
Said, A., & Pearlman, W.A. (1996). A new fast/efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and System Video Technology, 6(12), 243–250.CrossRefGoogle Scholar
Taubman, D. (2000). High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, 9(7), 1158–1170.CrossRefGoogle Scholar
Huang, W.B., Su, W.Y., & Kuo, Y.H. (2006). VLSI implementation of a modified efficient SPIHT encoder. IEICE Transactions on Fundamentals of Electronics, Communication and Computer Science, E89-A(12), 3613–3622.CrossRefGoogle Scholar
Jyotheswar, J., & Mahapatra, S. (2007). Efficient FPGA implementation of DWT and modified SPIHT for lossless image compression, Elsevier – Journal of Systems Architecture, 53(7), 369–378.CrossRefGoogle Scholar
Cho, Y., & Pearlman, W.A. (2007). Quantifying the coding performance of zerotrees of wavelet coefficients: Degree-k zerotree. IEEE Transactions on Signal Processing, 55(6), 2425–2431.MathSciNetCrossRefGoogle Scholar
Cicala L., & Poggi, G. (2007). A generalization of zerotree coding algorithms, Picture Coding Symposium 2007, Lisboa, Portugal.Google Scholar
Chew, L.W., Ang, L-M., & Seng, K.P. (2009). Lossless image compression using tuned Degree-K zerotree wavelet coding. Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS 2009), 1, 779–782.Google Scholar
Kassim, A.A., & Lee, W.S. (2003). Embedded color image coding using SPIHT with partially linked spatial orientation trees. IEEE Transactions on Circuits and Systems for Video Technology, 13(2), 203–206.CrossRefGoogle Scholar
Malvar, H., & Sullivan, G. (2003). YCoCg-R: A color space with RGB reversibility and low dynamic range, ISO/IEC JTC1/SC29/WG11 and ITU-T SG16 Q.6.Google Scholar
Shapiro, J.M. (1996). A fast technique for identifying zerotrees in the EZW algorithm, ICASSP-96, 3, 1455–1458.CrossRefGoogle Scholar