No-reference image quality assessment using statistics of sparse representations

The human visual system is sensitive to structural information in images, and modeling this information has been regarded as useful for predicting their perceptual quality. In this study, we propose a no-reference (NR) image quality assessment (IQA) method based on a sparse representation of the distribution of structural information. The grayscale fluctuation map of an image is first calculated and divided into patches of fixed size that are rearranged into column vectors, which are regarded as structural elements of the image. Following this, using sparse coding, these structural elements can be represented by sparse representation coefficients and a trained dictionary. By using the former, a probability vector for observing different elements in the trained dictionary can then be obtained. Finally, the prediction model is trained using support vector regression. The results of experiments to test the proposed method show that it can accurately predict humans’ perception of image quality and is competitive in comparison with prevalent NR–IQA methods.

No-reference image quality assessment in contourlet domain

Measuring visual quality, as perceived by human observers, is becoming increasingly important in many applications where humans are the ultimate consumers of visual information. Significant progress has been made for assessing the subjective quality of natural images, such as those taken by optical cameras. Natural Scene Statistics (NSS) is an important tool for no-reference visual quality assessment of natural images, where the reference image is not needed for comparison. In this paper, we take an important step towards using NSS to automate visual quality assessment of photorealistic synthetic scenes typically found in video games and animated movies. Our primary contributions are (1) conducting subjective tests on our publicly available ESPL Synthetic Image Database containing 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 format, and (2) evaluating the performance of 17 no-reference image quality assessment (IQA) algorithms using synthetic scene statistics. We find that similar to natural scenes, synthetic scene statistics can be successfully used for IQA and certain statistical features are good for certain image distortions.

An optimized CNN-based quality assessment model for screen content image

No-reference image quality assessment based on spatial and spectral entropies

Objective quality assessment metrics that are consistent with human judgments of image quality, play an important role in many image processing applications. No-reference image quality assessment (NR-IQA) algorithms evaluate the quality of distorted images without any information about the reference images. In this paper, we propose an accurate and simple method for NR-IQA. In this method local binary pattern (LBP) operator is first applied to subbands of wavelet decomposed image separately; then histogram of LBP codes is calculated and concatenated to form quality aware features. Finally, support vector regression (SVR) is utilized to map the feature vectors to subjective quality scores. The experimental results for LIVE and TID2008 databases show that our proposed method is highly correlated to the subjective scores, and competitive to state-of-the-art NR-IQA methods.

No-reference image quality assessment algorithms: A survey

Image contrast distortion is a common type of distortion in digital images. However, there is almost no research on the no-reference image quality assessment (NR-IQA) algorithm for image contrast. Therefore, we propose a histogram-based NR-IQA algorithm for contrast distorted images. Firstly, we analyze the image sequence with gradually changing contrast, and the image features are extracted from two aspects: objective statistical attribute, subjective perception attribute. And then we propose three statistical features, including image local contrast, histogram shape and image brightness, which can describe the image quality more simply and intuitively. Furthermore, we introduce the Just noticeable difference (JND) model, which makes the proposed algorithm have a higher matching degree between the human vision system (HVS) and the objective features in the algorithm. Finally, the support vector regression (SVR) is utilized to obtain the mapping relationship between the quantified features and the subjective scores to predict the quality of contrast distorted images. The outstanding performance of the proposed algorithm have been proved on CSIQ, TID2013 and CCID2014 databases.

Existing no-reference image quality assessment (NR-IQA) methods mainly focus on designing the low-level features related to image degradation. However, the evaluation of image quality is the human visual perception of image content, involving the integrated analysis of global high-level semantics and local low-level characteristics. From this perspective, we propose a NR-IQA framework based on global and local content perception. We adopt the deep convolutional neural network (DCNN) to extract the semantic feature implied in global image content. The perception of visual quality associated with local content utilizes the visual attention and filtering mechanisms of human visual system. The overall image quality is estimated by combining the semantic and local characteristic features generated from the perceptions. Experimental results on the LIVE IQA database demonstrate that our method is superior to the state-of-the-art NR-IQA algorithms and competitive to the popular full-reference IQA methods. Further experiments on the TID2008 dataset show that the proposed approach is robust for various kinds of distortion types.

In recent years, deep learning has achieved significant progress in many respects. However, unlike other research fields with millions of labeled data such as image recognition, only several thousand labeled images are available in image quality assessment (IQA) field for deep learning, which heavily hinders the development and application for IQA. To tackle this problem, in this paper, we proposed an error self-learning semi-supervised method for no-reference (NR) IQA (ESSIQA), which is based on deep learning. We employed an advanced full reference (FR) IQA method to expand databases and supervise the training of network. In addition, the network outputs of expanding images were used as proxy labels replacing errors between subjective scores and objective scores to achieve error self-learning. Two weights of error back propagation were designed to reduce the impact of inaccurate outputs. The experimental results show that the proposed method yielded comparative effect.

The adaptive optics (AO) technique is widely used to compensate for ocular aberrations and improve imaging resolution. However, when affected by intraocular scatter, speckle noise, and other factors, the quality of the retinal image will be degraded. To effectively improve the image quality without increasing the imaging system's complexity, the post-processing method of image deblurring is adopted. In this study, we proposed a conditional adversarial network-based method for directly learning an end-to-end mapping between blurry and restored AO retinal images. The proposed model was validated on synthetically generated AO retinal images and real retinal images. The restoration results of synthetic images were evaluated with the metrics of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), perceptual distance, and error rate of cone counting. Moreover, the blind image quality index (BIQI) was used as the no-reference image quality assessment (NR-IQA) algorithm to evaluate the restoration results on real AO retinal images. The experimental results indicate that the images restored by the proposed method have sharper quality and higher signal-to-noise ratio (SNR) when compared with other state-of-the-art methods, which has great practical significance for clinical research and analysis.

Images can be distorted in the real world via many sources like faulty sensors, artifacts generated by compression algorithms, defocus, faulty lens, and poor lighting conditions. Our biological vision system can identify the quality of image by looking at the images, but developing an algorithm to assess the quality of an image is a very challenging task as an image can be corrupted by different types of distortions and statistical properties of different types of distortions are dissimilar. The main objective of this article is to propose an image quality assessment technique for images corrupted by blurring and compression-based artifacts. Machine learning-based approaches have been used in recent times to perform this task. Images can be analyzed in different transform domains like discrete cosine transform domain, wavelet domains, curvelet domains, and singular value decomposition. These domains generate sparse matrices. In this paper, we propose no-reference image quality assessment algorithms for images corrupted by blur and different compression algorithms using sparsity-based features computed from different domains and all features pooled by support vector regression. The proposed model has been tested on three standard image quality assessment datasets LIVE, CSIQ, and TID2013, and correlation with subjected human opinion scores has been presented along with comparative study with state-of-the-art quality measures. Experiments run on standard image quality databases show that the results obtained are outperforming the existing results.

Our approach to no-reference image quality assessment (IQA) is grounded on the assumption that natural scenes have certain statistical properties which are altered in the presence of distortion. Therefore, by measuring this statistic one can recognize the distortion affecting the image and provide a no-reference (NR) IQA. In this paper, we extract local entropy and standard deviation from non-overlapping 8 × 8 blocks in the image and its gradient version from 2 scales. Extracting features from gradient images can help to better distinguishing blur and noise effects on images. Having extracted local measures, we aggregate them by finding the parameters of a Gaussian mixture model fitted on these local measures. Finally, our quality metric is achieved via a Neural Network machine learning tool, and its performance is evaluated on a standard LIVE database. The experimental results point out that our proposed features not only can identify the type of distortion but also are consistent with the DMOS in subjective assessment in terms of the correlation coefficient.

In this work, we propose a supervised no-reference (NR) Image Quality Assessment (IQA) model for the objective evaluation of the perceptual quality of 3D virtual reality (VR) images. To achieve such practical algorithm, we first study the scene statistics of saliency maps of the individual left and right views of VR images, and empirically model these statistics with Univariate Generalized Gaussian Distribution (UGGD). We compute the UGGD model parameters at multi-scale and multi-orient steerable subband decomposition, and introduce these features as distortion discriminables. This is followed by the computation of the entropy and normalized root mean square scores of each subband and then these values are utilized as weights to pool the individual view features. We apply the popular 2D supervised BRISQUE model on the individual views to estimate the overall spatial quality of VR images. As the last step of the algorithm, the predicted saliency score and the spatial BRISQUE score are pooled to derive the final quality score of VR images. The performance of the proposed model is evaluated on the popular LIVE 3D VR IQA dataset. The results indicate robust and competitive performance against the off-the-shelf 2D full-reference and NR supervised algorithms.

Prior knowledge about anticipated distortions and their corresponding human opinion scores is needed in the most general purpose no-reference image quality assessment algorithms. When creating the model, all distortion types may not be existed. Predicting the quality of distorted images in practical no-reference image quality assessment algorithms is devised without prior knowledge about images or their distortions. In this study, a blind/no-reference opinion and distortion unaware image quality assessment algorithm based on natural scenes is developed. The proposed approach uses a set of novel features to measure image quality in a spatial domain. The extracted features which are from the scenes gist are formed using Weibull distribution statistics. When testing the proposed algorithm on LIVE database, experiments show that it correlates well with subjective opinion scores. They also show that the proposed algorithm significantly outperforms the popular full-reference peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) methods. Not only do the results reasonably well compete with the recently developed natural image quality evaluator (NIQE) model, but also outperform it.