A Novel Generalized DCT-Based JND Profile Based on an Elaborate CM-JND Model for Variable Block-Sized Transforms in Monochrome Images

In this paper, we propose a new DCT-based just noticeable difference (JND) profile incorporating the spatial contrast sensitivity function, the luminance adaptation effect, and the contrast masking (CM) effect. The proposed JND profile overcomes two limitations of conventional JND profiles: 1) the CM JND models in the conventional JND profiles employed simple texture complexity metrics, which are not often highly correlated with perceived complexity, especially for unstructured patterns. So, we proposed a new texture complexity metric that considers not only contrast intensity, but also structureness of image patterns, called the structural contrast index. We also newly found out that, as the structural contrast index of a background texture pattern increases, the modulation factors for CM-JND show a bandpass property in frequency. Based on this observation, a new CM-JND is modeled as a function of DCT frequency and the proposed structural contrast index, showing significantly high correlations with measured CM-JND values and 2) while the conventional DCT-based JND profiles are only applicable for specific transform block sizes, our proposed DCT-based JND profile is first designed to be applicable to any size of transform by deriving a new summation effect function, which can also be appropriately applied for quad-tree transform of high efficiency video coding. For the overall performance, the proposed DCT-based JND profile shows more tolerance for distortions with better perceptual quality than other JND profiles under comparison.