  We propose a reinforcement learning approach for real-time exposure control
of a mobile camera that is personalizable. Our approach is based on Markov
Decision Process (MDP). In the camera viewfinder or live preview mode, given
the current frame, our system predicts the change in exposure so as to optimize
the trade-off among image quality, fast convergence, and minimal temporal
oscillation. We model the exposure prediction function as a fully convolutional
neural network that can be trained through Gaussian policy gradient in an
end-to-end fashion. As a result, our system can associate scene semantics with
exposure values; it can also be extended to personalize the exposure
adjustments for a user and device. We improve the learning performance by
incorporating an adaptive metering module that links semantics with exposure.
This adaptive metering module generalizes the conventional spot or matrix
metering techniques. We validate our system using the MIT FiveK and our own
datasets captured using iPhone 7 and Google Pixel. Experimental results show
that our system exhibits stable real-time behavior while improving visual
quality compared to what is achieved through native camera control.
