Are Standard CNNs Good Enough for No-Reference Stereoscopic Image Quality Assessment?

Perceptual quality metrics derived from deep features have led to a boost in modelling the Human Visual System (HVS) to perceive the quality of visual content. In this work, we study the effectiveness of fine-tuning three standard convolutional neural networks (CNNs) viz. ResNet50, VGG16 and MobileNetV2 to predict the quality of stereoscopic images in the no-reference setting. This work also aims to understand the impact of using disparity maps for quality prediction. Interestingly, our experiments demonstrate that disparity maps do not significantly contribute to improving perceptual quality estimation in the deep learning framework. To the best of our knowledge, this is the first study that explores the impact of disparity along with the chosen models for Stereoscopic Image Quality Assessment. We present a detailed study of our experiments with various architectural configurations on the LIVE Phase I and II datasets. Further, our results demonstrate the innate capability of deep features for quality prediction. Finally, the simple fine-tuning of the models results in solutions that compete with state-of-the-art patch-based stereoscopic image quality assessment methods.