基于WDFB及形态膨胀的图像压缩算法

Image Compression Algorithm Based on Wavelet-Directional Filter Banks and Morphological Dilation

将小波和方向滤波器组结合,实现了一种非冗余的图像变换WDFB,它满足各向异性尺度关系,能更稀疏地表示诸如边缘和纹理等几何特征。利用WDFB的优势,提出一种基于形态学操作的有效的图像压缩算法。该算法利用重要树来表达子带间的相关性,同时采用形态膨胀算子来聚类子带内的重要系数。实验结果表明,新算法在PSNR指标上明显优于基于小波的压缩算法,尤其对于含有丰富纹理的图像。例如对于512×512的Barbara图,在0.25bpp压缩率下,新算法比SPIHT和MRWD算法的PSNR分别提高1.08dB和0.87dB。

Combined wavelet with directional filter banks, a non-redundant image transform called as Wavelet-Directional Filter Banks （WDFB） was realized, It fulfilled the anisotropy scaling law and could represent the geometrical features such as edges and texture more sparsely. By taking the advantages of the WDFB, an efficient image compression algorithm based on morphology operator was proposed. The new algorithm used the significant tree to express the correlation of inter-band while utilizing morphological dilation operator to cluster the significant coefficients within sub-band. Experimental results show that the performance of the new algorithm is superior to the wavelet-based algorithms in terms of Peak Signal to Noise Ratio （PSNR）, especially for the images including a large portion of texture. For example, for Barbara image of 512×512, at 0.25bpp, PSNR of the new algorithm outperforms that of SPIHT and MRWD by 1.05dB and 0.77dB, respectively.