Robust statistics and no-reference image quality assessment in Curvelet domain
This is an incremental improvement for image processing applications, enhancing quality assessment without reference images.
The paper tackles the problem of no-reference image quality assessment by using robust statistics and curvelet transform to predict human opinions on degraded images, showing a gain in performance compared to a prior method when tested on three datasets.
This paper uses robust statistics and curvelet transform to learn a general-purpose no-reference (NR) image quality assessment (IQA) model. The new approach, here called M1, competes with the Curvelet Quality Assessment proposed in 2014 (Curvelet2014). The central idea is to use descriptors based on robust statistics to extract features and predict the human opinion about degraded images. To show the consistency of the method the model is tested with 3 different datasets, LIVE IQA, TID2013 and CSIQ. To test evaluation, it is used the Wilcoxon test to verify the statistical significance of results and promote an accurate comparison between new model M1 and Curvelet2014. The results show a gain when robust statistics are used as descriptor.