  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
