Enhanced subband JND model with textural image

Just Noticeable Difference (JND) model in transform domain is determined by the contrast sensitivity function, luminance masking and contrast masking. In this paper, we propose an improved JND model with a new method for contrast masking factor estimation. We decompose an image into structural image and textural image, and the textural image is used for an accurate block classification. The proposed algorithm can remove the interference from the edge pixels thus the accurate JND in DCT domain is obtained. Experimental results show that the proposed algorithm can improve the JND thresholds and reduce more data redundancy than the relevant existing JND estimators. This model can be widely used in many image/video processing fields, such as perceptual video coding.