Research on Compression Performance Prediction of JPEG2000

Abstract

Image compression is one of the potential techniques for image processing. However, the compression also takes a certain amount of time, and some algorithms are not adapted to all images. In order to improve the processing efficiency and performance, this paper studied the relation between image characteristics and Peak Signal to Noise Ratio (PSNR) to predict the compression performance. In this paper, we adopted JPEG2000 algorithm to compress, and PSNR to evaluate the image quality. The statistics of an image contains the values of mean, variance, entropy and others. Then, we drew the relation graph between each statistic calculated and PSNR, and found the statistic which is the most closely related to PSNR. Finally, we derived an explicit expression. Experimental results show that Image Activity Measure (IAM) has the closest relation with PSNR, and the expression has the average relative error of 2% - 3.0%. Meanwhile, it can be simplified by ensuring that the formula error is unchanged basically. Furthermore, we also used other images dataset for verifying the formula. Gray images all can be well predicted for JPEG2000 algorithm when Compression Ratio (CR) is 16. It indicates that a more accurate and simpler IAM-PSNR relation we had obtained. Therefore, we can predict the compression performance before compression so as to select the appropriate compression algorithm and to provide great convenience for subsequent processing.