In autonomous driving applications, cameras are a vital sensor as they can provide structural, semantic and navigational information about the environment of the vehicle. While image quality is a concept well understood for human viewing applications, its definition for computer
vision is not well defined. This gives rise to the fact that, for systems in which human viewing and computer vision are both outputs of one video stream, historically the subjective experience for human viewing dominates over computer vision performance when it comes to tuning the image signal
processor. However, the rise in prominence of autonomous driving and computer vision brings to the fore research in the area of the impact of image quality in camera-based applications. In this paper, we provide results quantifying the accuracy impact of sharpening and contrast on two image
feature registration algorithms and pedestrian detection. We obtain encouraging results to illustrate the merits of tuning image signal processor parameters for vision algorithms.