A saliency-modulated just-noticeable-distortion model with non-linear saliency modulation functions

It is well-known that the human visual system (HVS) cannot sense small variations of visual signals below the so-called just-noticeable distortion (JND) thresholds due to their underlying spatial/temporal masking properties. It is also known that the visual attention mechanism of the human brain can enhance or reduce visual sensitivity. In other words, the visual attention has modulatory effects on JND thresholds. The current knowledge also states that the visual attention is mainly driven by visual saliency in an automatic and involuntary manner. In this paper we present a saliency-modulated JND (SJND) model for static images in the discrete cosine transform (DCT) domain. In the proposed model, the JND thresholds of each block in a given image are elevated by two non-linear modulation functions using the visual saliency of the block. The parameters of the saliency modulation functions are obtained through an optimization framework, which utilizes a state-of-the-art saliency-based objective image quality assessment method. To evaluate the proposed SJND model, two subjective experiments were conducted. The obtained experimental results demonstrated that the proposed method achieves a high accuracy in JND estimation, and also it provides a high distortion-hiding capacity.