  Depth adjustment aims to enhance the visual experience of stereoscopic 3D
(S3D) images, which accompanied with improving visual comfort and depth
perception. For a human expert, the depth adjustment procedure is a sequence of
iterative decision making. The human expert iteratively adjusts the depth until
he is satisfied with the both levels of visual comfort and the perceived depth.
In this work, we present a novel deep reinforcement learning (DRL)-based
approach for depth adjustment named VCA-RL (Visual Comfort Aware Reinforcement
Learning) to explicitly model human sequential decision making in depth editing
operations. We formulate the depth adjustment process as a Markov decision
process where actions are defined as camera movement operations to control the
distance between the left and right cameras. Our agent is trained based on the
guidance of an objective visual comfort assessment metric to learn the optimal
sequence of camera movement actions in terms of perceptual aspects in
stereoscopic viewing. With extensive experiments and user studies, we show the
effectiveness of our VCA-RL model on three different S3D databases.
