联合Contourlet和Bandelet的变换在图像压缩中的应用

Application of joint Contourlet and Bandelet transforms in image compression

针对小波变换方向选择性差的局限,提出了一种基于Contourlet的Bandelet变换.该变换首先通过小波变换把图像分解成不同频率的子带,接着用方向滤波器组把高频子带分解为多个方向子带,之后对每个方向子带进行Bandelet变换.这种多方向多尺度临界采样的变换能更稀疏地表示图像边缘和纹理等几何特征,有利于图像压缩.将JPEG2000的比特位平面和上下文编码方法应用到量化后的变换系数上实现了图像压缩.实验结果表明,对于纹理和边缘丰富的图像,提出的压缩算法优于JPEG2000.

The wavelet transform suffers from the limitation of poor directional selectivity. To overcome this disadvantage, a novel scheme with ioint Contourlet and Bandelet transform is proposed. First the image is decomposed into different frequency subbands by the wavelet transform, and then the high frequency subband is further decomposed into directional subbands by directional filterbank. Next, the Bandelet transform is taken on each directional subband. The proposed multidirectional and multiscale transform with critical sampling can represent the geometrical features such as edges and texture more sparsely, which is of great benefit to image compression. Finally, the bit plane and contextual coding procedure of the JPEG2000 scheme are applied to quantized transform coefficients to realize image compression. Experiments show that the proposed algorithms evidently outperform JPEG2000 for the images with abundant edges and texture.