End-to-end deep multi-score model for No-reference stereoscopic image quality assessment
This work addresses the problem of accurately assessing stereoscopic image quality without reference for applications in 3D imaging, though it is incremental as it builds on existing deep learning approaches.
The paper tackles stereoscopic image quality assessment by proposing a deep multi-score CNN that predicts left, right, stereo, and global quality scores to handle asymmetric distortions, achieving superior results compared to state-of-the-art methods on Waterloo IVC 3D databases.
Deep learning-based quality metrics have recently given significant improvement in Image Quality Assessment (IQA). In the field of stereoscopic vision, information is evenly distributed with slight disparity to the left and right eyes. However, due to asymmetric distortion, the objective quality ratings for the left and right images would differ, necessitating the learning of unique quality indicators for each view. Unlike existing stereoscopic IQA measures which focus mainly on estimating a global human score, we suggest incorporating left, right, and stereoscopic objective scores to extract the corresponding properties of each view, and so forth estimating stereoscopic image quality without reference. Therefore, we use a deep multi-score Convolutional Neural Network (CNN). Our model has been trained to perform four tasks: First, predict the left view's quality. Second, predict the quality of the left view. Third and fourth, predict the quality of the stereo view and global quality, respectively, with the global score serving as the ultimate quality. Experiments are conducted on Waterloo IVC 3D Phase 1 and Phase 2 databases. The results obtained show the superiority of our method when comparing with those of the state-of-the-art. The implementation code can be found at: https://github.com/o-messai/multi-score-SIQA