Blind image quality assessment through anisotropy

We describe an innovative methodology for determining the quality of digital images. The method is based on measuring the variance of the expected entropy of a given image upon a set of predefined directions. Entropy can be calculated on a local basis by using a spatial/ spatial-frequency distribution as an approximation for a probability density function. The generalized Rényi entropy and the normalized pseudo-Wigner distribution (PWD) have been selected for this purpose. As a consequence, a pixel-by-pixel entropy value can be calculated, and therefore entropy histograms can be generated as well. The variance of the expected entropy is measured as a function of the directionality, and it has been taken as an anisotropy indicator. For this purpose, directional selectivity can be attained by using an oriented 1-D PWD implementation, Our main purpose is to show how such an anisotropy measure can be used as a metric to assess both the fidelity and quality of images. Experimental results show that an index such as this presents some desirable features that resemble those from an ideal image quality function, constituting a suitable quality index for natural images. Namely, in-focus, noise-free natural images have shown a maximum of this metric in comparison with other degraded, blurred, or noisy versions. This result provides a way of identifying in-focus, noise-free images from other degraded versions, allowing an automatic and nonreference classification of images according to their relative quality. It is also shown that the new measure is well correlated with classical reference metrics such as the peak signal-to-noise ratio. © 2007 Optical Society of America.This research has been supported by the following projects: TEC2004-00834, TEC2005-24739-E, TEC2005- 24046-E, and 20045OE184 from the Spanish Ministry of Education and Science and PI040765 from the Spanish Ministry of Health.Peer Reviewe

Regression-free Blind Image Quality Assessment

Regression-based blind image quality assessment (IQA) models are susceptible to biased training samples, leading to a biased estimation of model parameters. To mitigate this issue, we propose a regression-free framework for image quality evaluation, which is founded upon retrieving similar instances by incorporating semantic and distortion features. The motivation behind this approach is rooted in the observation that the human visual system (HVS) has analogous visual responses to semantically similar image contents degraded by the same distortion. The proposed framework comprises two classification-based modules: semantic-based classification (SC) module and distortion-based classification (DC) module. Given a test image and an IQA database, the SC module retrieves multiple pristine images based on semantic similarity. The DC module then retrieves instances based on distortion similarity from the distorted images that correspond to each retrieved pristine image. Finally, the predicted quality score is derived by aggregating the subjective quality scores of multiple retrieved instances. Experimental results on four benchmark databases validate that the proposed model can remarkably outperform the state-of-the-art regression-based models.Comment: 11 pages, 7 figures, 50 conference

Blind Image Quality Assessment for Face Pose Problem

No-Reference image quality assessment for face images is of high interest since it can be required for biometric systems such as biometric passport applications to increase system performance. This can be achieved by controlling the quality of biometric sample images during enrollment. This paper proposes a novel no-reference image quality assessment method that extracts several image features and uses data mining techniques for detecting the pose variation problem in facial images. Using subsets from three public 2D face databases PUT, ENSIB, and AR, the experimental results recorded a promising accuracy of 97.06% when using the RandomForest Classifier, which outperforms other classifier

Test Time Adaptation for Blind Image Quality Assessment

While the design of blind image quality assessment (IQA) algorithms has improved significantly, the distribution shift between the training and testing scenarios often leads to a poor performance of these methods at inference time. This motivates the study of test time adaptation (TTA) techniques to improve their performance at inference time. Existing auxiliary tasks and loss functions used for TTA may not be relevant for quality-aware adaptation of the pre-trained model. In this work, we introduce two novel quality-relevant auxiliary tasks at the batch and sample levels to enable TTA for blind IQA. In particular, we introduce a group contrastive loss at the batch level and a relative rank loss at the sample level to make the model quality aware and adapt to the target data. Our experiments reveal that even using a small batch of images from the test distribution helps achieve significant improvement in performance by updating the batch normalization statistics of the source model.Comment: Accepted to ICCV 202

A Shift-Dependent Measure of Extended Cumulative Entropy and Its Applications in Blind Image Quality Assessment

Recently, Tahmasebi and Eskandarzadeh introduced a new extended cumulative entropy (ECE). In this paper, we present results on shift-dependent measure of ECE and its dynamic past version. These results contain stochastic order, upper and lower bounds, the symmetry property and some relationships with other reliability functions. We also discuss some properties of conditional weighted ECE under some assumptions. Finally, we propose a nonparametric estimator of this new measure and study its practical results in blind image quality assessment