Iterative Thresholding-Based Sparse Directional Representation for Efficient Low Bit-Rate Embedded Video Coding
The contourlet transform provides a flexible directional image decomposition by employing Laplacian pyramid and uniform directional filter banks. Although it is able to efficiently capture the 2-D piecewise smooth functions with line or curve discontinuities, its major drawbacks of 4/3 redundancy and non-ideal filter banks in Laplacian pyramid are the main obstacles for high performance image/video coding. In this paper, we propose a video coding scheme based on the sharp frequency localized contourlet transform (contourlet/SFL) under the sparse representation framework, in which the iterative thresholding algorithm is applied to get an l1-norm sparser version of the transform coefficients. In the meantime, considering the fact that strong inter-band and inter-scale dependencies exist in the contourlet/SFL coefficients, a directional embedded image coding system is proposed to propagate the significance status by using the neighbor, cousin and parent significant coefficients as seeds. Experimental results show that the coding performance and visual quality of the sparse contourlet/SFL based video compression scheme are superior to the wavelet-based one, especially on those sequences full of directional structures at low bit rates.