DynamicISP: Dynamically Controlled Image Signal Processor for Image Recognition
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Image signal processor (ISP) plays an important role not only for human perceptual quality but also for computer vision. In most cases, experts resort to manual tuning of many parameters in the ISPs for perceptual quality. It failed in sub-optimal, especially for computer vision. Aiming to improve ISPs, two approaches have been actively proposed; tuning the parameters with machine learning, or constructing an ISP with DNN. The former is lightweight but lacks expressive powers. The latter has expressive powers but it was too heavy to calculate on edge devices. To this end, we propose DynamicISP, which consists of traditional simple ISP functions but their parameters are controlled dynamically per image according to what the downstream image recognition model felt to the previous frame. Our proposed method successfully controlled parameters of multiple ISP functions and got state-of-the-art accuracy with a small computational cost.
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