  In this paper, we propose a multi-objective camera ISP framework that
utilizes Deep Reinforcement Learning (DRL) and camera ISP toolbox that consist
of network-based and conventional ISP tools. The proposed DRL-based camera ISP
framework iteratively selects a proper tool from the toolbox and applies it to
the image to maximize a given vision task-specific reward function. For this
purpose, we implement total 51 ISP tools that include exposure correction,
color-and-tone correction, white balance, sharpening, denoising, and the
others. We also propose an efficient DRL network architecture that can extract
the various aspects of an image and make a rigid mapping relationship between
images and a large number of actions. Our proposed DRL-based ISP framework
effectively improves the image quality according to each vision task such as
RAW-to-RGB image restoration, 2D object detection, and monocular depth
estimation.
