No- Reference Image Quality Assessment for Intelligent Sensing Applications

Many intelligent sensing systems rely heavily on automatic analysis tools to extract high level information from the raw videos or images captured by cameras. In particular, deep-learning-based computer vision solutions have shown promising results in analysis tasks ranging from image segmentation to object detection and recognition. In practical systems, image distortions due to factors such as noise and blur may degrade the accuracy of these analysis tools. This paper proposes a no-reference image quality assessment model for predicting the quality of images from the perspective of three major computer vision tasks: image segmentation, image classification, and object detection. A data set is constructed that considers distortions including noise, blur, and bad lighting, which commonly occur during the image acquisition process in diverse applications. Three widely used deep-learning-based algorithms are considered to label the quality of the images in the dataset. A set of light-weight features are extracted to characterize the structure of the content in an image. Based on the data set and the extracted features, a classification model is built to predict the quality of images used in computer vision tasks. Experimental results show that the proposed model offers more accurate predictions than common image quality measures such as BRISQUE, NIQE, and PIQE.