Impact analysis of digital watermarking on perceptual quality of image and video using Just Noticeable Difference

In this project, two improved perceptual quality methods are proposed. One is weighted PSNR (WPSNR). Based on the fact that the human eye will have less sensitivity to modifications in textured areas than in smooth areas, WPSNR uses an additional parameter called the Noise Visibility Function (NVF) that is a texture masking function as a penalization factor. The other method based on Watson HVS models. In his work, Watson defines Just Noticeable Difference (JND) as linear multiples of a noise pattern that produces a JND distortion measure. A perceptually lossless quantization matrix of the DCT transform is generated. Each entries of this matrix represent the amount of quantization that each coefficient can withstand without affecting the visual quality of the image. Introduction All sorts of digital watermarking algorithms or methods have grown greatly in the past twenty years. However, besides designing these approaches, a very important and often neglected issue is how to effectively and precisely measure the perceptual quality of image that has been watermarked. In other words, we need quality metrics to analyze the image degradation introduced by embedding watermarks. However, most popular measures in the field of image coding and compression such as MSE, SNR and PSNR, are objective and independent of the subjective factors like HVS (human visual system). This might be a problem in the application of digital watermarking since sophisticated watermarking methods exploit in one way or the other HVS. Using the above measures to quantify the distortion caused by a watermarking process might therefore result in misleading quantitative distortion measurements. In particular, in the digital watermarking field, the difference between original image and watermarked image is very small. The evaluation of image quality is significantly affected by image contents. Therefore, the present objective measures without considering the effect of HVS do not always provide with reliable quality assessments. In this project, section I briefly introduced some fundamental knowledge regarding human visual system and visual models proposed by Watson [1993]. Section II mainly talked about two current types of measures for perceptual quality, including subjective and objective assessments. And two improved measures which take into account the effect of HVS are proposed in this section. In section III, we evaluate the perceptual quality of different images through several simulations using three perceptual measures (PSNR, WPSNR and JND-based).