A New Measure of Image Scrambling Degree Based on Grey Level Difference and Information Entropy

Digital image scrambling is an effective tool in data hiding, digital watermarking, and digital image encryption. In order to evaluate the image scrambling performance effectively, a new normalized measure of image scrambling degree based on the grey level difference and the general information entropy of the scrambled image is proposed. This new method measures the image scrambling degree from both the local discreteness and the global uniformity by combining three aspects of an image: the discreteness, the uniformity of the discreteness, and the randomness in statistical distribution of the image, so it is more rational than any unilateral way. Experiments on the images scrambled by Arnold transformation, and the sub-affine transformation and a compound scrambling method based on chaotic maps show that the new measure has better consistency with the inspection result of human visual system than that of the existing measures.