  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.
