Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of acquisition, compression, transmission, storage, and display, which might heavily degrade the image quality and thus strongly reduce the final display effect and clarity. To verify the reliability of existing image quality assessment methods, we establish a new industrial process image database (IPID), which contains 3000 distorted images generated by applying different levels of distortion types to each of the 50 source images. We conduct the subjective test on the aforementioned 3000 images to collect their subjective quality ratings in a well-suited laboratory environment. Finally, we perform comparison experiments on IPID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types.

翻译：近年来，图像处理技术在烟雾检测、安全监控及工件检测等多工业流程中的应用范围日益广泛。在图像采集、压缩、传输、存储与显示过程中，不同种类和等级的失真会被引入图像，这严重降低了图像质量，进而削弱最终显示效果与清晰度。为验证现有图像质量评估方法的可靠性，我们建立了一个新型工业流程图像数据库（IPID），该数据库包含3000幅失真图像，这些图像由50张原始图像分别施加不同失真类型与等级生成。我们在适合的实验室环境下对上述3000幅图像开展主观测试，收集其主观质量评分。最后，在IPID数据库上通过对比实验研究若干客观图像质量评估算法的性能。实验结果表明，现有最先进的图像质量评估方法在预测包含多重失真类型的图像质量时仍存在困难。