Stereoscopic Visual Discomfort Prediction Using Multi-scale DCT Features

Prior approaches to the problem of visual discomfort prediction (VDP) for stereo/3D images are built for the uncompressed image. This paper presents a novel VDP method based on the compressed image by using multi-scale discrete cosine transform (MsDCT). Three types of visual discomfort features, including basic disparity intensity (BDI), disparity gradient energy (DGE) and disparity texture complexity (DTC), are extracted from two-dimensional (2-D) DCT coefficients. Additionally, a multi-scale transformation approach based on the different sizes of transform units is applied to obtain the multi-scale sub-features for each of the features. Then, through experimental comparison, a random forest regressor is chosen to fuse twenty-three sub-features to get the final objective prediction value of the S3D images. Experimental results conducted on two datasets show that the proposed method improves the prediction accuracy compared to those of recent S3D visual (dis)comfort predictors.