R-MSSIM: Image quality assessment while performing object detection

摘要

As deep learning continues to mature, researchers have increasingly turned to deep neural network to process object detection in images. While the accuracy of object detection continues to increase, researchers can analyze object features in images better. Image quality assessment is an important research topic in computer vision. When analyzing and processing images, it will lead to more computing overhead if different tasks are performed separately. It can save computing overhead that combining object detection and quality assessment. In order to process quality assessment better while detecting object and to improve the efficiency of image processing, we introduce R-MSSIM, a new system of image quality assessment stronger based on region proposal and structural similarity measure. R-MSSIM can improves the performance of classical algorithms greatly based on object defection. R-MSSIM fits the features of local regions to the final mean opinion score(MOS) while detecting region proposals in an image, which makes the interpretability of image quality assessment stronger. Finally, we prove that our method has a large performance improvement compared with the classical algorithm through a series of experiments, and achieved relatively good performance.