Blind Image Quality Prediction for Object Detection
Blind Image Quality Prediction for Object Detection
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DOI：
10.1109/mipr.2019.00046
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Lingchao Kong;A. Ikusan;Rui Dai;Jingyi Zhu
中科院分区：
文献类型：
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作者：
Lingchao Kong;A. Ikusan;Rui Dai;Jingyi Zhu
自动视频数据分析工具已成为当今成像应用中不可或缺的组件。自动分析方法的准确性取决于所处理的图像或视频的质量。因此，有必要引入客观指标来预测自动分析算法评估的图像质量。目标检测是自动视频分析过程中的第一步，也是最重要的一步。本文提出了一种新的图像质量模型来预测目标检测的性能。构建视频数据集时考虑了与成像过程中质量下降相关的不同因素，例如图像分辨率降低、噪声和模糊。针对该数据集获得了常用的低复杂度目标检测算法的性能。构建基于回归树装袋集成的无参考回归模型，以使用图像中的可观察特征来预测对象检测的准确性。实验结果表明，与 PSNR 和 SSIM 等众所周知的图像质量度量相比，所提出的模型为目标检测提供了更准确的图像质量预测。
Automatic video data analysis tools have become indispensable components in today's imaging applications. The accuracy of automatic analysis methods relies on the quality of images or videos that are processed. It is therefore essential to introduce objective metrics for predicting the quality of images as evaluated by automatic analysis algorithms. Object detection is the first and the most important step in the process of automatic video analysis. This paper proposes a new image quality model for predicting the performance of object detection. A video data set is constructed that considers different factors related to quality degradation in the imaging process, such as reduced image resolution, noise, and blur. The performances of commonly used low-complexity object detection algorithms are obtained for the data set. A no-reference regression model based on a bagging ensemble of regression trees is built to predict the accuracy of object detection using observable features in an image. Experimental results show that the proposed model provides more accurate predictions of image quality for object detection than commonly known image quality measures such as PSNR and SSIM.