Efficiency of Jpeg Image Compression Using Quantization Noise Analysis

The basic idea behind this paper is the efficient JPEG image compression using quantization noise analysis. Based on the analysis of noises in multiple-cycle JPEG compression, we define a quantity called forward quantization noise. We analytically derive that a decompressed JPEG image has a lower variance of forward quantization noise than its uncompressed counterpart. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. On the analysis of noise in JPEG compression, our proposed method is based on forward quantization noise by quantizing the DCTblocks coefficients with step by one. Decompressed JPEG image has a lower noise variance than the uncompressed. The main aim is to identify a compression of JPEG image with high quality. The main purpose of image compression is to reduce the redundancy and irrelevancy present in the image, so that it can be stored and transferred efficiently. The compressed image is represented by less number of bits compared to original. Hence, the required storage size will be reduced, consequently maximum images can be stored and it can transferred in faster way to save the time.