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.
Examples of NR IQA work using non-parametric statistics associated with the Curvelet transform can be found in @cite_2 @cite_10 . On the other hand, in the literature review, the authors did not find any work in NR IQA that made use of robust statistical tools to extract features from the Curvelets space.
@cite_10 @cite_2
1537865522 1989735895
We develop an efficient general-purpose blind no-reference image quality assessment (IQA) algorithm that utilizes curvelet domain features of phase congruency values and local spectral entropy values in distorted images. A 2-stage framework of distortion classification followed by quality assessment is used for mapping feature vectors to prediction scores. We utilize a support vector machine (SVM) to train an image distortion and quality prediction model. The resulting algorithm which we name Phase Congruency and Spectral Entropy based Quality (PCSEQ) index is capable of assessing the quality of distorted images across multiple distortion categories. We explain the advantages of phase congruency features and spectral entropy features. We also thoroughly test the algorithm on the LIVE IQA databse and find that PCSEQ correlates well with human judgments of quality. It is superior to the full-reference (FR) IQA algorithm SSIM and several top-performance no-reference (NR) IQA methods such as DIIVINE and SSEQ. We also tested PCSEQ on the TID2008 database to ascertain whether it has performance that is database independent. In this paper, we propose a new general Quality Assessment method based on the curvelet transform, called Curvelet No-Reference (CNR) model, which can estimate levels of noise, blur and JPEG 2000 compression of natural images. The peak coordinate of the curvelet coefficient histogram occupies distinctive regions depending on how the image was modified from the original. During training, we associate peak positions with known filter levels. In the prediction stage, the filter levels of new images are estimated from the training data, with no access to the reference images. We tested CNR both on our own image dataset and on LIVE [4]. Results demonstrate that CNR does a better job at predicting noise and blur levels among several methods, including SSIM [1] and PSNR. We also present an accelerated version of CNR that does not sacrifice prediction accuracy on natural images.