Toward Domain Transfer for No-Reference Quality Prediction of Asymmetrically Distorted Stereoscopic Images

We have presented a no-reference quality prediction method for asymmetrically distorted stereoscopic images, which aims to transfer the information from source feature domain to its target quality domain using a label consistent K-singular value decomposition classification framework. To this end, we construct a category-deviation database for dictionary learning that assigns a label for each stereoscopic image to indicate if it is noticeable or unnoticeable by human eyes. Then, by incorporating a category consistent term into the objective function, we learn view-specific feature and quality dictionaries to establish a semantic framework between the source feature domain and the target quality domain. The quality pooling is comparatively simple and only needs to estimate the quality score based on the classification probability. The experimental results demonstrate the effectiveness of our blind metric.