Image Coding Algorithm Based On Complex Contourlet Transform

One of the main problems, which confronted with multimedia technology, is to solve the denotation, transmission and storage of the image data information. In order to obtain satisfying vision and transmission effect, effective image data compress coding, is the key technology to solve. We focus on how to reconstruct the original image with least distortion, especially on the retain of textures and contours.Nowadays, wavelet-based image coding technique is the main trend in image compression field. However, wavelets can not restore the detailed texture information which has distinct directional property. N.G Kingsbury proposes a new transform, which called Dual Tree Complex Wavelet, with improved directionality, however, and it also can not efficiently represent and process multimensional information. Contourlet transform is an efficient anisotropy analysis tool in high dimensions. Compared to wavelets, contourlets not only possess the main features of directionality and anisotropy, but also can effectively capture the intrinsic geometrical structures such as smooth contours in natural images.In this paper, firstly, the classic image coding algorithms in wavelet domain are introduced. Secondly, that we focus on this paper is, Contourlet Transform brings in 4/3 redundancy, which is not suitable for image compression, and this redundancy arising from Laplacian Pyramid filter. Based on this situation, we use Dual Tree Complex Wavelet as muti-scale decomposition tool, and use DFB as high-level direction decomposition tool. And then, with reasonable quantification, we change the six-band factor generated by the Dual Tree Complex Wavelet transform into traditional direction of the three sub-band coefficient, and the encoding scheme is realized by the idea of SPIHT. In this way, we successfully combined Dual Tree Complex Wavelet transform and Contourlet transform into one transform that we call it Complex Contourlet transform. At the end, this algorithm is concluded, theoretical and practical significance of this paper is obviously.Experimental results show that complex contourlet transform algorithms improve the quality of the reconstructed image when compare to classic or advanced schemes. Our algorithm possesses the features of interoperability and adaptability, which has excellent performance in both PSNR and perceptual results.