A New Proposed Method For A Statistical Rules-Based Digital Image Compression

Abstract

Image compression depends on data compression of digital images. Its central objective is to decrease the redundancy of the image data for reducing space and the cost of transmitting data in public communication channels. This research suggests a new compression technique based on the statistical rules. The proposed technique is one of the lossy compression techniques is based upon the statistics of the pixel values of the gray-scale image. The quality of these compressed images have been evaluated using some factors like the Image size before/after the compression process, Compression Ratio, (CR), and Peak Signal to Noise Ratio, (PSNR), Mean Square Error (MSE), and Mean Space Saving (MSS). Experimental results have demonstrated that the proposed technique provides sufficient higher compression with minimal to lose data.

Method for Processing and Assessing the Degree of Digital Image Compression Based on Haar Transformation

“…Conversion coefficient sensitivity coef S divided into local and global [10]. With global sensitivity, the transformation coefficient is a function of all coordinates of the input sequence space, and for local sensitivity, only a certain part of the coordinates.…”

“…Thus, from the analysis of digital image compression procedures, it follows that in order to meet the requirements for the efficiency of image processing and transmission when implementing methods for compressing them with an acceptable level of distortion, it is necessary to carry out an orthogonal transformation of the original image. Table 1 also shows the values of the proposed performance indicators for the considered transformations [10,[12][13][14]. The use of WT according to the Haar basis provides the smallest increase in the dynamic range (1.5 times), when using other bases, the increase will be greater.…”