Implementational aspects of the contourlet filter bank and application in image coding
EURASIP Journal on Advances in Signal Processing
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
The multiscale directional filter bank (MDFB) improves the radial frequency resolution of the contourlet transform by introducing an additional decomposition in the high-frequency band. The increase in frequency resolution is particularly useful for texture description because of the quasi-periodic property of textures. However, the MDFB needs an extra set of scale and directional decomposition, which is performed on the full image size. The rise in computational complexity is, thus, prominent. In this paper, we develop an efficient implementation framework for the MDFB. In the new framework, directional decomposition on the first two scales is performed prior to the scale decomposition. This allows sharing of directional decomposition among the two scales and, hence, reduces the computational complexity significantly. Based on this framework, two fast implementations of the MDFB are proposed. The first one can maintain the same flexibility in directional selectivity in the first two scales while the other has the same redundancy ratio as the contourlet transform. Experimental results show that the first and the second schemes can reduce the computational time by 33.3%-34.6% and 37.1%-37.5%, respectively, compared to the original MDFB algorithm. Meanwhile, the texture retrieval performance of the proposed algorithms is more or less the same as the original MDFB approach which outperforms the steerable pyramid and the contourlet transform approaches.