Description

Here we propose a novel method in the compression of images. In preprocessing we remove the noise from the image. After removing the noise, the image will be quantized by discrete wavelet transform. Discrete wavelet transforms which is used to decompose the images. It converts the image from spatial domain to frequency domain. The CS encoder is made signal independent and computationally inexpensive by shifting the bulk of system complexity to the decoder. While these properties of CS allow signal acquisition and communication in some severely resource-deprived conditions that render conventional sampling and coding impossible, they are accompanied by rather disappointing rate–distortion performance. After quantization we encode the image by the use of SPIHT (set partitioning in hierarchical trees). This will generate the coded value for an image. After codes will be given to decoding process. After decoding it will given to the dequantization for reconstruct the images. It is Inverse wavelet Transform. It reconstructs the original image.