Efficiency Increasing of No-reference Image Quality Assessment in UAV Applications

Abstract

Unmanned aerial vehicle (UAV) imaging is a dynamically developing field, where the effectiveness of imaging applications highly depends on quality of the acquired images. Noreference image quality assessment is widely used for quality control and image processing management. However, there is a lack of accuracy and adequacy of existing quality metrics for human visual perception. In this paper, we demonstrate that this problem persists for typical applications of UAV images. We present a methodology to improve the efficiency of visual quality assessment by existing metrics for images obtained from UAVs, and introduce a method of combining quality metrics with the optimal selection of the elementary metrics used in this combination. A combined metric is designed based on a neural network trained to utilize subjective assessments of visual quality. The metric was tested using the TID2013 image database and a set of real UAV images with embedded distortions. Verification results have demonstrated the robustness and accuracy of the proposed metric.