A new just-noticeable-distortion model combined with the depth information and its application in multi-view video coding

Abstract

Traditional video compression methods remove spatial and temporal redundancy based on the statistical correlation. However the final receptor is the human, we can remove the perception redundancy to get higher compression efficiency, by taking use of the properties of human visual system (HVS). Research has simulated the sensitivity of HVS to luminance contrast and spatial and temporal masking effects with the just-noticeable-distortion (JND) model, which describes the perception redundancy quantitatively. This paper proposes a new model named MJND (JND in Multi-view), which explores the property of HVS to stereoscopic masking effect. The proposed model not only contains the spatial and temporal JND, but also includes the JND in depth. The MJND model is then used in macroblock (MB) quantization adjustment and rate-distortion optimization in multi-view video coding (MVC). Compared with the standard MVC scheme without JND, our model can get better visual quality in the case of the same bit rate.