A 3D Subjective Quality Prediction Model Based On Depth Distortion

Depth map quality plays an important role in 3D subjective quality. In this paper, a 3D subjective quality prediction model is proposed to estimate the 3D quality of synthesized stereopairs based on depth map distortion and neural mechanism, instead of performing view synthesis directly, which benefits 3D processing. In order to build the model, a dataset is first built to include distinctive distortion features for depth map coding, and then a subjective test is conducted. Based on the subjective evaluation results, a prediction model is built to estimate the perceived 3D quality of the synthesized stereopairs with the features extracted from the texture characteristics of decoded depth maps and neural population coding model. In terms of the correlation coefficient with actual 3D subjective quality, the proposed model outperforms the conventional 2D QA metrics applied to the depth maps as well as that applied to the stereopairs.