Skip to main content
Log in

Segment-based coding of color images

  • Published:
Science in China Series F: Information Sciences Aims and scope Submit manuscript

Abstract

Based on the idea of second generation image coding, a novel scheme for coding still images is presented. At first, an image was partitioned with a pulse-coupled neural network; and then an improved chain code and the 2D discrete cosine transform was adopted to encode the shape and the color of its edges respectively. To code its smooth and texture regions, an improved zero-trees strategy based on the 2nd generation wavelet was chosen. After that, the zero-tree chart was selected to rearrange quantified coefficients. And finally some regulations were given according to psychology of various users. Experiments under noiseless channels demonstrate that the proposed method performs better than those of the current one, such as JPEG, CMP, EZW and JPEG2000.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Wang X H, Zhang F Y. Research process on still image coding. J Comput Res Develop, 2001, 38(11): 1315–1326

    Google Scholar 

  2. Wu L N. Data Compression. 2nd ed. Beijing: Press of Electronic and Industry, 2005

    Google Scholar 

  3. Chen D Q, Zhao Y M, Wan C M. Region and edge segmentation based highly scalable coding method on color image. Infrared Laser Eng, 2004, 33(6): 634–645

    Google Scholar 

  4. Zhao Y M, Wan C M. High scalable coding method on color image. J Shanghai Jiaotong Univ, 2004, 38(9): 1496–1499

    Google Scholar 

  5. Daubechies I. Ortho-normal bases of compactly supported wavelets. Commun Pure Appl Math, 1988, 410(XII): 909–996

    Article  MathSciNet  Google Scholar 

  6. Shapiro J M. Embedded image coding using zero-trees of wavelet coefficients. IEEE Trans Image Process, 1993, 41(12): 3445–3462

    MATH  Google Scholar 

  7. Schwab B H. Secure Identification System. US Patent: 7418474, 1999

  8. Egger O, Li W. Very low bit rage image coding using morphological operators and adaptive decompositions. In: Proc of the IEEE Int’l Conf. on Image Processing, 1994. 326–330

  9. Kunt M, Ikonomopoulos A, Kocher M. Second-generation image coding techniques. Proc IEEE, 1985, 73(4): 549–574

    Article  Google Scholar 

  10. Kocher M, Kunt M. Image data compression by contour texture modeling. In: SPIE Proc of the Int’l Conf on the Applications of Digital Image Processing, Geneva, Switzerland, 1983. 131–139

  11. Carlsson S. Sketch-based coding of grey level images. Sig Process, 1988, 15(1): 57–83

    Article  Google Scholar 

  12. Mallat S G, Zhong S. Characterization of signals from multi-scale edges. IEEE Trans Pattern Anal Mach Intell, 1992, 14(7): 710–732

    Article  Google Scholar 

  13. Froment J, Mallat S G. Second generation compact image coding with wavelets. In: Chuied Wavelets—A Tutorial in Theory and Applications, New York: Academic Press, 1992

    Google Scholar 

  14. Eckhorn R, Reitboeck H H, Arndt M, et al. Feature linking via synchronization among distributed assemblies: Simulation of results from cat cortex. Neural Comput, 1990, 2(3): 293–307

    Article  Google Scholar 

  15. Vlatko B. Image object classification using saccadic search, spatio-temporal pattern encoding and self-organisation. Pattern Recog Lett, 2000, 21(3): 253–263

    Article  Google Scholar 

  16. Karina W, Thomas L, Vlatko B, et al. Patterns from the sky: Satellite image analysis using pulse coupled neural networks for pre-processing, segmentation and edge detection. Pattern Recog Lett, 2000, 21(3): 227–237

    Article  Google Scholar 

  17. Hu D Y, Xie Z H. X-ray sky image segmentation using improved pulse coupled neural networks. J Xi’an Univ Arts Sci, 2007, 10(3): 21–25

    Google Scholar 

  18. Xiong X M, Wang Y M, Zhang X C, et al. Locust detection by image segmentation based on pulse coupled neural network. J Agricul Mech Res, 2007, (1): 180–183

  19. Luan Z Q, Diao M, Zhao Z J. Research on fingerprint image segmentation based on pulse coupled neural networks. Appl Sci Tech, 2006, 33(10): 25–27

    Google Scholar 

  20. Shi M H, Zhang J Y, Zhang X B, et al. Image binary segmentation based on image PCNN model. Comput Simul, 2002, 19(4): 42–46

    Google Scholar 

  21. Park K H, Park H W. Region-of-interest coding based on set partitioning in hierarchical trees. IEEE Trans Circuits Syst Video Tech, 2002, 12(2): 106–113

    Article  Google Scholar 

  22. Pinho A J. Region based near loseless image compression. In: Proc of the IEEE Int Conf on Acoustics, Speech, and Signal Processing. Utah: Salt Lake City, 2001. 1761–1764

    Google Scholar 

  23. Tang Y, Mo Y L. Second generation wavelet transform applied to lossless compression coding for image. J Imag Graph, 55(8): 699–702

  24. Yao Q D, Bi H J, Wang Z H, et al. Fundamental of Image Coding. 3rd ed. Beijing: Tsinghua University Press

  25. Salembier P. Morphological multi-scale segmentation for image coding. Sig Processg, 1994, 38: 359–386

    Article  Google Scholar 

  26. Salembier P, Torres L, Meyer F, et al. Region-based video coding using mathematical morphology. Proc IEEE, 1995, 86(6): 843–857

    Article  Google Scholar 

  27. Li J L, Chen G, Man J J. Fuzzy integral based objective assessment of coding quality of color image. J Comput-Aided Des Comput Graph, 2005, 17(8): 1823–1827

    Google Scholar 

  28. Han F F, Zhang B F, Jiang X, et al. Implementation of image EZW algorithm on high-speed DSP. Chinese J Sci Instrument, 2006, 27(6): 864–866

    Google Scholar 

  29. Fu W X, Xi L H, Dou P, et al. Implementation of improved EZW image coding algorithm. J Jilin Univ (Inf Sci Ed), 2004, 22(2): 93–97

    Google Scholar 

  30. Wen J, Villasensor J D. A class of reversible variable-length codes for robust image and video coding. In: Proc 1997 IEEE Int’l Conf Image Processing. Santa Barbara, CA, 1999. 65–68

  31. Ferguson T J, Rabinowitz J H. Self synchronization Huffman codes. IEEE Trans Inf Theory, 1984, 30(4): 687–693

    Article  MathSciNet  MATH  Google Scholar 

  32. Zhang Y Q, Liu Y J, Pickholtz R. Layered image transmission over cellular radio channels. IEEE Trans Consum Electr, 1993, 39(3): 455–460

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to YuDong Zhang.

Additional information

Supported by the Senior University Technology Innovation Essential Project Cultivation Fund Project (Grant No. 706028) and the Natural Science Fund of Jiangsu Province (Grant No. BK2007103)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, Y., Wu, L. Segment-based coding of color images. Sci. China Ser. F-Inf. Sci. 52, 914–925 (2009). https://doi.org/10.1007/s11432-009-0019-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-009-0019-7

Keywords

Navigation