Performance evaluation of traditional and adaptive lifting based wavelets with SPIHT for lossy image compression

Nowadays wavelet transform has been one of the most effective transform means in the realm of image processing, especially the biorthogonal 9/7 wavelet filters proposed by Daubechies, which have good performance in image compression. Hence, in this paper an attempt has been made to analyse traditional and adaptive lifting based wavelet techniques for image compression. The original image is transformed using adaptive lifting based CDF 9/7 wavelet transform and traditional CDF 9/7 followed by it is compressed using Set Partitioning In Hierarchical Tree algorithm (SPIHT) and the performance was compared with the popular traditional CDF9/7 wavelet transform. The performance metric Peak Signal to Noise Ratio (PSNR) for the reconstructed image was computed. The proposed adaptive lifting algorithm give better performance than traditional CDF9/7 wavelet, the most popular wavelet transforms. Lifting allows us to incorporate adaptivity and nonlinear operators into the transform. The proposed methods efficiently represent the edges and appear promising for image compression. The proposed adaptive methods reduce edge artifacts and ringing and give improved PSNR of 4.69 to 6.09 dB than the traditional CDF 9/7 for edge dominated 2D images.