Content complexity based just noticeable difference estimation in DCT domain
Just noticeable difference (JND), which reveals the visibility of our human visual system (HVS), is useful for image/video coding. Due to the content complexity, it is hard to accurately estimate the JND thresholds for different image blocks (e.g., edge and texture). Research on cognitive science indicates that the HVS is adaptive to extract the visual regularities for scene perception and understanding. Inspired by this, we analyze the effect of content complexity on visual masking, and calculate the content complexity with the distribution of orientation. Then, by considering the effect from content complexity, contrast sensitivity function, and luminance adaption, a novel JND model in Discrete Cosine Transform (DCT) domain is proposed. Experimental results demonstrate the effectiveness of the proposed model for JND estimation.