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The aim of no-reference image quality assessment (NR-IQA) is to assess the quality of an image, which is consistent with the mean opinion score, without any prior knowledge about the reference image. This work proposes a new NR-IQA technique based on natural scene statistics properties of the bag-of-features representation and feature selection algorithms. The proposed bag-of-features technique utilizes Harris affine detector and scale invariant feature transform to compute points, which are clustered using the k-means clustering algorithm to extract features for IQA. The extracted features are utilized with a support vector regression model to assess the quality of the image. The proposed technique outperforms state-of-the-art NR-IQA techniques, when tested on three commonly used subjective image quality assessment databases. The experimental results have shown that the features extracted using the proposed technique are database independent and shows high correlation with the mean opinion score. [ABSTRACT FROM AUTHOR]

Human stress is a serious health concern, which must be addressed with appropriate actions for a healthy society. This paper presents an experimental study to ascertain the appropriate phase, when electroencephalography (EEG) based data should be recorded for classification of perceived mental stress. The process involves data acquisition, pre-processing, feature extraction and selection, and classification. The stress level of each subject is recorded by using a standard perceived stress scale questionnaire, which is then used to label the EEG data. The data are divided into two (stressed and non-stressed) and three (non-stressed, mildly stressed, and stressed) classes. The EEG data of 28 participants are recorded using a commercially available four channel Muse EEG headband in two phases i.e., pre-activity and post-activity. Five feature groups, which include power spectral density, correlation, differential asymmetry, rational asymmetry, and power spectrum are extracted from five bands of each EEG channel. We propose a new feature selection algorithm, which selects features from appropriate EEG frequency band based on classification accuracy. Three classifiers i.e., support vector machine, the Naive Bayes, and multi-layer perceptron are used to classify stress level of the participants. It is evident from our results that EEG recording during the pre-activity phase is better for classifying the perceived stress. An accuracy of $\text{92.85}\%$ and $\text{64.28}\%$ is achieved for two- and three-class stress classification, respectively, while utilizing five groups of features from theta band. Our proposed feature selection algorithm is compared with existing algorithms and gives better classification results. [ABSTRACT FROM AUTHOR]

Images play an essential part in our daily lives and the performance of various imaging applications is dependent on the user's quality of experience. No-reference image quality assessment (NR-IQA) has gained importance to assess the perceived quality, without using any prior information of the nondistorted version of the image. Different NR-IQA techniques that utilize natural scene statistics classify the distortion type based on groups of features and then these features are used for estimating the image quality score. However, every type of distortion has a different impact on certain sets of features. In this paper, a new feature selection algorithm is proposed for distortion identification based image verity and integration evaluation that selects distinct feature groups for each distortion type. The selection procedure is based on the contribution of each feature on the Spearman rank order correlation constant (SROCC) score. Only those feature groups are used in the prediction model that have majority features with SROCC score greater than mean SROCC score of all the features. The proposed feature selection algorithm for NR-IQA shows better performance in comparison to state-of-the-art NR-IQA techniques and other feature selection algorithms when evaluated on three commonly used databases. [ABSTRACT FROM AUTHOR]

Blind image quality assessment (BIQA) is a challenging task in real-world problems due to unavailability of reference images. The performance of BIQA techniques is highly dependent on features used to assess the image quality. In the literature, different BIQA techniques have been proposed using a two-step approach, i.e., feature extraction in different domains and prediction of quality score using extracted features. However, optimum feature selection for these techniques has not been explored. This paper investigates the impact of feature selection algorithms on the performance of BIQA techniques. In contrast to existing techniques, the proposed methodology follows a three-step approach. Firstly, features are extracted using existing BIQA techniques. In the second step, feature selection algorithm is applied on the extracted features to reduce the number of features. The selected features are then utilized for prediction of a quality score in the third step. The proposed approach is evaluated for six BIQA techniques using five commonly used feature selection algorithms. Experimental results show that the feature selection algorithms not only reduces the number of features but also improves the performance of the state-of-the-art BIQA techniques. [ABSTRACT FROM AUTHOR]