DNN-based ISP Parameter Inference Algorithm for Automatic Image Quality Optimization

Abstract

In camera development, because the image quality is subjective and the tuning complexity is increasing, building a correlated model with image signal processor (ISP) pipeline is very demanding task. In order to overcome those problems, this paper proposes an automatic image quality tuning framework based on Deep Neural Network (DNN). The image quality metric (IQM) have been defined to quantifies subjective image quality, which effectively represents the actual user perception. In this way, fast reproduction of the desired image has been possible through the minimized computing resource. Proposed Optimization methodology consists of Phase 1, a ISP modeling, and Phase 2, parameter optimization. Phase 1 construct a model between the parameters of ISP and the image quality metric. At phase 2, we add partially connected layer at input layer in order to optimize the parameters of ISP. Using backpropagation approach, the network selectively updates only the weights of partial connections, which allow to automatically derive the optimal parameters for high quality image. This idea has been implemented and experimented through commercial 16 Mega pixel resolution CMOS image sensor (CIS) and the state-of-the art ISP.