Camera Exposure Control for Robust Robot Vision with NoiseAware Image Quality Assessment In this paper, we propose a noiseaware exposure control algorithm for robust robot vision. Our method aims to capture bestexposed images, which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric that captures complementary quality attributes and ensures lightweight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows the preservation of sharp edges and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a realtime and fully automatic exposure and gain control technique based on the NelderMead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates the higher qualitative and quantitative performance compared with conventional approaches.

Limitation and Challenges of Image Quality Measurement Subjectivelyrated image databases have become increasingly popular in the evaluation of image quality measurement algorithms. Several groups recently have improved their metricsu0027 performance in matching these databases, using particular HVS (human visual system) properties or image statistical models. However, it is difficult to know whether these improvements are due to progress towards mimicking the perceptual properties, or are due to matching some characteristics of the databases. This paper demonstrates an inherent limitation in using such databases, showing that our very simple metric, built on the contrast masking effect, is able to perform as good as many stateoftheart metrics. It is also argued that existent databases neither contain enough images with particularly biased distortions to test the significance of single HVS property, nor cover diverse distortion types to reflect the requirement of emerging applications.