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.
The use of FDCT with 2-stages SVM was proposed by @cite_5 . @cite_10 proposed an NR IQA method that uses the entropy of the Curvelet transform coefficients, also associated with the 2-stages SVM. Ahmed e Der @cite_8 used the method proposed in @cite_5 with the spatial features for study of contrast in images. Recently @cite_12 use the model Curvelet2014 @cite_5 to quantify the quality of images associated with historical documents.
@cite_5 @cite_10 @cite_12 @cite_8
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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. Contrast is a very important characteristic for visual perception of image quality. Some No-Reference Image Quality Assessment Algorithm NR-IQA metrics for Contrast-Distorted Images (CDI) have been proposed in the literature, e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQACDI). Here, we intend to improve the assessment results of images available in databases such as TID2013 and CSIQ. Most of the NR-IQA metrics (e.g. NR-IQACDI) designed for CDI adopt features available in the spatial domain. This paper proposes to compliment it with feature in Curvelet domain which is powerful in capturing multiscale and multidirectional information in an image. We employed the Natural Scene Statistics (NSS) features in Curvelet domain originally recommended by (2014) which were found useful in the assessment of the quality of image distorted by compression, noise and blurring. Experiments were then conducted to assess the effect of incorporating these NSS features. The experimental results based on K-fold cross validation (K ranged from 2 to 10) and statistical test showed that the performance of NRIQACDI was improved. Future works include improvements of NRIQACDI, exploration of feature fusion methods and using a suitable feature selection method. Abstract We study the efficacy of utilizing a powerful image descriptor, the curvelet transform, to learn a no-reference (NR) image quality assessment (IQA) model. A set of statistical features are extracted from a computed image curvelet representation, including the coordinates of the maxima of the log-histograms of the curvelet coefficients values, and the energy distributions of both orientation and scale in the curvelet domain. Our results indicate that these features are sensitive to the presence and severity of image distortion. Operating within a 2-stage framework of distortion classification followed by quality assessment, we train an image distortion and quality prediction engine using a support vector machine (SVM). The resulting algorithm, dubbed CurveletQA for short, was tested on the LIVE IQA database and compared to state-of-the-art NR FR IQA algorithms. We found that CurveletQA correlates well with human subjective opinions of image quality, delivering performance that is competitive with popular full-reference (FR) IQA algorithms such as SSIM, and with top-performing NR IQA models. At the same time, CurveletQA has a relatively low complexity. This paper addresses the problem of general-purpose No-Reference Image Quality Assessment (NR-IQA) with the goal of developing a real-time, cross-domain model that can predict the quality of distorted images without prior knowledge of non-distorted reference images and types of distortions present in these images. The contributions of our work are two-fold: first, the proposed method is highly efficient. NR-IQA measures are often used in real-time imaging or communication systems, therefore it is important to have a fast NR-IQA algorithm that can be used in these real-time applications. Second, the proposed method has the potential to be used in multiple image domains. Previous work on NR-IQA focus primarily on predicting quality of natural scene image with respect to human perception, yet, in other image domains, the final receiver of a digital image may not be a human. The proposed method consists of the following components: (1) a local feature extractor, (2) a global feature extractor and (3) a regression model. While previous approaches usually treat local feature extraction and regression model training independently, we propose a supervised method based on back-projection, which links the two steps by learning a compact set of filters which can be applied to local image patches to obtain discriminative local features. Using a small set of filters, the proposed method is extremely fast. We have tested this method on various natural scene and document image datasets and obtained state-of-the-art results.