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

Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images