Disparity-based just-noticeable-difference model for perceptual stereoscopic video coding using depth of focus blur effect

Abstract

Human 3D perception provides an important clue to the removal of redundancy in stereoscopic 3D (S3D) videos. Because objects outside the binocular fusion limit cannot be fused on retina, the human visual system (HVS) makes them blur according to the depth-of-focus (DOF) effect to increase the binocular fusion limit and suppress diplopia, i.e. double vision. Based on human depth perception, we propose a disparity-based just-noticeable-difference model (DJND) to save bit-rate and improve visual comfort in S3D videos. We combine the DOF blur effect with conventional JND models in the pixel domain into DJND. Firstly, we use disparity information to get the average disparity value of each block. Then, we integrate the DOF blur effect into luminance JND (LJND) by a selective low pass Gaussian filter to minimize the visual stimulus in S3D videos. Finally, we incorporate disparity information into the filtered JND models to obtain DJND. Experimental results demonstrate that the proposed method successfully improves both image quality and visual comfort in viewing S3D videos without increasing the bit-rate.