Comparisons of Image Compression Using Different Transformation Techniques

For image compression, it is very necessary that the selection of transform should reduce the size of the resultant data as compared to the original data set. For continuous and discrete time cases, wavelet transform and wavelet packet transform has emerged as popular techniques. While integer wavelet using the lifting scheme significantly reduces the computation time, a completely new approach for further speeding up the computation. First, wavelet packet transforms (WPT) and lifting scheme (LS) are used. Then an application of the LS to WPT is presented which leads to the generation of integer wavelet packet transform (IWPT).another technique is used for image compression is fractal image compression (FIC) is based on the partitioned iterated function system (PIFS) which utilizes the self-similarity property in the image to achieve the purpose of compression., the linear regression technique from robust statistics is embedded into the encoding procedure of the fractal image compression. Another drawback of FIC is the poor retrieved image qualities when compressing corrupted images, the fractal image compression scheme should be insensitive to those noises presented in the corrupted image. This leads to a new concept of robust fractal image compression.Another technique for image compression is new multi-layered representation technique for image compression, which combine Curvelet transform and local DCT in order to benefit from the advantages of each. Curvelet transform is one of the recently developed multiscale transform, which possess directional features and provides optimally sparse representation of objects with edges, but not for the textured feature. We exploit morphological component analysis (MCA) method to separate the image into two layers: piecewise smooth layer and textured structure layer, respectively associated to curvelet transform and local DCT. Each layer is encoded independently with a different transform at a different bit rate. In this paper we are using following transformation techniques: 1. Discrete Cosine Transform (DCT) 2. Wavelet Packet Transform (WPT) 3. Discrete Wavelet Transform ( DWT)