TR2007-015

Video Coding Using 3D Dual-Tree Wavelet Transform


    •  Wang, B., Wang, Y., Selesnick, I., Vetro, A., "Video Coding Using 3D Dual-Tree Wavelet Transform", EURASIP Journal on Image and Video Processing, Vol. 2007, January 2007.
      BibTeX TR2007-015 PDF
      • @article{Wang2007jan,
      • author = {Wang, B. and Wang, Y. and Selesnick, I. and Vetro, A.},
      • title = {Video Coding Using 3D Dual-Tree Wavelet Transform},
      • journal = {EURASIP Journal on Image and Video Processing},
      • year = 2007,
      • volume = 2007,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2007-015}
      • }
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  • Research Area:

    Digital Video

Abstract:

This work investigates the use of the 3D dual-tree discrete wavelet transform (DDWT) for video coding. The 3D DDWT is an attractive video representation because it isolates image patterns with different spatial orientations and motion directions and speeds in separate subbands. However, it is an over complete transform with 4:1 redundancy when only real parts are used. We apply the noise-shaping algorithm proposed by Kingsbury to reduce the number of coefficients. To code the remaining significant coefficients, we propose two video codecs. The first one applies separate 3D set partitioning in hierarchical trees (SPIHT) on each subset of the DDWT coefficients (each forming a standard isotropic tree). The second codec exploits the correlation between redundant subbands, and codes the subbands jointly. Both codecs do not require motion compensation and provide better performance than the 3D SPIHT codec using the standard DWT, both objectively and subjectively. Furthermore, both codecs provide full scalability in spatial, temporal, and quality dimensions. Besides the standard isotropic decomposition, we propose an anisotropic DDWT, which extends the superiority of the normal DDWT with more directional subbands without adding to the redundancy. This anisotropic structure requires significantly fewer coefficients to represent a video after noise shaping. Finally, we also explore the benefits of combining the 3D DDWT with the standard DWT to capture a wider set of orientations.

 

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