Measuring Natural Scenes SFR of Automotive Fisheye Cameras
This addresses image quality assessment for automotive computer vision, but it appears incremental as it adapts an existing method to a specific camera type.
The paper tackled the problem of unknown image quality metrics in public datasets for automotive fisheye cameras by adapting the Natural Scenes Spatial Frequency Response algorithm for wide field-of-view cameras, but no concrete results or numbers are provided.
The Modulation Transfer Function (MTF) is an important image quality metric typically used in the automotive domain. However, despite the fact that optical quality has an impact on the performance of computer vision in vehicle automation, for many public datasets, this metric is unknown. Additionally, wide field-of-view (FOV) cameras have become increasingly popular, particularly for low-speed vehicle automation applications. To investigate image quality in datasets, this paper proposes an adaptation of the Natural Scenes Spatial Frequency Response (NS-SFR) algorithm to suit cameras with a wide field-of-view.