Channel noise reduction for compressed images using singular value decomposition

The paper is based on channel noise estimation and its reduction in compressed images using singular value decomposition. Image compression reduces irrelevant and redundant image data so that image is stored in lesser space and can be transmitted efficiently. Reducing the storage area increases the capacity of storage medium as well as the channel bandwidth. However, when the compressed image is transmitted, noise is added to it via transmission channel and the image is distorted. Therefore, accurate noise estimation and its reduction in a wide variety of vision and image processing applications is an important issue. An efficient algorithm has been developed which is based on the study of singular values of noise corrupted images and estimates the noise level in images that is further used for setting up a threshold for wavelet denoising. This dependency of threshold value on the esimation of noise level results in a better quality of denoised image. This algorithm has been applied to JPEG and JPEG2000 compressed images and corresponding results have been analyzed in terms of parameters like MSE and PSNR. The algorithm is more reliable, shows robust behavior over a visual content and noise conditions and it is more efficient as compared to the other relevant existing methods.