Adaptive Multiple Description Depth Image Coding Based on Wavelet Sub-band Coefficients

  • Jingyuan Ma
  • Huihui BaiEmail author
  • Meiqin Liu
  • Dongxia Chang
  • Rongrong Ni
  • Yao Zhao
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 82)


With the development of multi-view video plus depth technology, the coding algorithm at the depth image has become one of hot research directions. As we know, after wavelet transform, the energy of the image smoothing region is concentrated in low frequency sub-band, while the edge information of the texture region is concentrated in high frequency sub-bands. However, the edge information is very important to the synthetic viewpoint. In order to improve the edge decoding quality and ensure the transmission reliability, we propose an adaptive multiple description depth image coding scheme based on wavelet sub-band coefficients. The low frequency sub-band is encoded by optimized multiple description lattice quantization (OMDLVQ), while the high frequency sub-bands are encoded by embedded block coding with dead-zone. Finally, two streams of the vector quantization are combined with the embedded block coding stream respectively. The experimental results show that this scheme has good performance in transmission reliability and reconstructed image quality.


Depth image Wavelet transform MDLVQ SPECK 



This work was supported in part by National Natural Science Foundation of China (No. 61672087, 61402033) and CCF-Tencent Open Fund.


  1. 1.
    Lee, J.Y., Wey, H.C., Park, D.S.: A fast and efficient multi-view depth image coding method based on temporal and inter-view correlations of texture images. IEEE Trans. Circ. Syst. Video Technol. 21, 1859–1868 (2011)CrossRefGoogle Scholar
  2. 2.
    Li, G., Qu, W., Huang, Q.: A multiple targets appearance tracker based on object interaction models. IEEE Trans. Circ. Syst. Video Technol. 22, 450–464 (2012)CrossRefGoogle Scholar
  3. 3.
    Wang, M., et al.: Region of interest oriented fast mode decision for depth map coding in DIBR. In: Proceedings of the 2011 IEEE 7th International Colloquium on Signal Processing and its Applications, pp. 177–180 (2011)Google Scholar
  4. 4.
    Wang, T., et al.: Depth map coding based on adaptive block compressive sensing. In: IEEE China Summit and International Conference on Signal and Information Processing, pp. 492–495 (2015)Google Scholar
  5. 5.
    Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9, 889–896 (2000)CrossRefGoogle Scholar
  6. 6.
    Servetto, S.D., et al.: Multiple description wavelet based image coding. IEEE Trans. Image Process. 9, 813–826 (2000)CrossRefGoogle Scholar
  7. 7.
    Bai, H., Zhu, C., Zhao, Y.: Optimized multiple description lattice vector quantization for wavelet image coding. IEEE Trans. Circ. Syst. Video Technol. 17, 912–917 (2007)CrossRefGoogle Scholar
  8. 8.
    Zhang, H., Bai, H., Zhao, Y.: Optimized multiple description lattice vector quantization coding for 3D depth image. KSII Trans. Int. Inf. Syst. 9, 1140–1154 (2015)Google Scholar
  9. 9.
    Said, A., Pearlman, W.A.: A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circ. Syst. Video Technol. 6, 243–250 (1996)CrossRefGoogle Scholar
  10. 10.
    Islam, A., Pearlman, W.A.: Embedded and efficient low-complexity hierarchical image coder. In: Proceedings of SPIE the International Society for Optical Engineering, pp. 385–388 (2000)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jingyuan Ma
    • 1
  • Huihui Bai
    • 1
    Email author
  • Meiqin Liu
    • 1
  • Dongxia Chang
    • 1
  • Rongrong Ni
    • 1
  • Yao Zhao
    • 1
  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBejingChina

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