Automatic ISP image quality tuning using non-linear optimization

TL;DR

This paper introduces an automatic image quality tuning method for Image Signal Processors using nonlinear optimization, significantly reducing tuning time and working across various processing blocks without needing detailed implementation knowledge.

Contribution

The paper presents a novel automatic IQ tuning approach that leverages nonlinear optimization and reference generation, enabling rapid and versatile tuning of ISP blocks.

Findings

Achieves high-quality IQ tuning in minutes instead of weeks.

Works with multiple ISP processing blocks like noise reduction, demosaic, and sharpening.

Does not require knowledge of specific algorithm implementations.

Abstract

Image Signal Processor (ISP) comprises of various blocks to reconstruct image sensor raw data to final image consumed by human visual system or computer vision applications. Each block typically has many tuning parameters due to the complexity of the operation. These need to be hand tuned by Image Quality (IQ) experts, which takes considerable amount of time. In this paper, we present an automatic IQ tuning using nonlinear optimization and automatic reference generation algorithms. The proposed method can produce high quality IQ in minutes as compared with weeks of hand-tuned results by IQ experts. In addition, the proposed method can work with any algorithms without being aware of their specific implementation. It was found successful on multiple different processing blocks such as noise reduction, demosaic, and sharpening.