A wavelet video coding algorithm with balanced significance probability tree based on energy weighting

  • Chuan-Ming Song
  • Bo Fu
  • Xiang-Hai WangEmail author
  • Ming-Zhe Fu


This work presents a 3-D wavelet video coding algorithm. By analyzing the contribution of each biorthogonal wavelet basis to reconstructed signal’s energy, we weight each wavelet subband according to its basis energy. Based on distribution of weighted coefficients, we further discuss a 3-D wavelet tree structure named balanced significance probability tree, which places the coefficients with similar probabilities of being significant on the same layer. It is implemented by using hybrid spatial orientation tree and temporal-domain block tree. Subsequently, a novel 3-D wavelet video coding algorithm is proposed based on the energy-weighted balanced significance probability tree. Experimental results illustrate that our algorithm always achieves good reconstruction quality for different classes of video sequences. Compared with asymmetric 3-D orientation tree, the average peak signal-to-noise ratio (PSNR) gain of our algorithm are 1.24dB, 2.54dB and 2.57dB for luminance (Y) and chrominance (U,V) components, respectively. Compared with temporal-spatial orientation tree algorithm, our algorithm gains 0.38dB, 2.92dB and 2.39dB higher PSNR separately for Y, U, and V components. In addition, the proposed algorithm requires lower computation cost than those of the above two algorithms.


Video coding Scalable coding 3-D wavelet transform Weighted tree Significant coefficient 



This work is supported by the National Natural Science Foundation of China (NSFC) under Grant nos. 61402214, 41671439, and 61702246, the Provincial Natural Science Foundation of Liaoning under Grant no. 20180550570, the Program for Liaoning Innovative Research Talents in University, the Open Foundation of State Key Laboratory for Novel Software Technology of Nanjing University under Grant no. KFKT2018B07, and the Dalian Foundation for Youth Science and Technology Star under Grant no. 2015R069.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Chuan-Ming Song
    • 1
    • 2
    • 3
  • Bo Fu
    • 1
  • Xiang-Hai Wang
    • 1
    Email author
  • Ming-Zhe Fu
    • 1
  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianChina
  2. 2.School of Computer Science and TechnologyDalian University of TechnologyDalianChina
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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