The field of image processing has several applications in our daily life. The image quality can be affected by a wide variety of deformations during image acquisition, transmission, compression, etc. Image compression is one of the applications where the quality of the image plays an important role since it can be used to evaluate the performance of various image compression techniques. Many image quality assessment metrics have been proposed. This paper proposes a new metric to assess the quality of compressed images. The principle idea of this metric is to estimate the amount of lost information during image compression process using three components: error magnitude, error location and error distribution. We denote this metric as MLD, which combines the objective assessment (error magnitude) and the subjective assessment (error location and error distribution). First, the metric is used to estimate the quality of compressed images using the JPEG algorithm as this is a standard lossy image compression technique. Then, the metric is used to estimate the quality of compressed images using other compression techniques. The results illustrate that the proposed quality metric is correlated with the subjective assessment better than other well-known objective quality metrics such as SSIM, MSE and PSNR. Moreover, using the proposed metric the JPEG2000 algorithm produces better quality results as compared to the JPEG algorithm especially for higher compression ratios
Image compression involves reducing the size of image data file, while retaining necessary information.This paper uses the facilities of the Genetic Algorithm for the enhancement of the performance of one of the popular compression method, Vector Quantization method is selected in this work. After studying this method, new proposed algorithm for mixing the Genetic Algorithm with this method was constructed and then the required programs for testing this algorithm was written. The proposed algorithm was tested by applying it on some image data files. Some fidelity measures are calculated to evaluate the performance of the new proposed algorithm. A good enhancement was recorded for the performance of the Vector Quantization method when mixed with the Genetic Algorithm. All programs were written by using Matlab (version 7.0) and these programs were executed on the Pentium III (866 MHz) personal computer.