Traditional blind image quality assessment (IQA) measures generally predict quality from a sole distorted image directly. In this paper, we first introduce multiple pseudo reference images (MPRIs) by further degrading the distorted image in several ways and to certain degrees, and then compare the similarities between the distorted image and the MPRIs. Via such distortion aggravation, we can have some references to compare with, i.e., the MPRIs, and utilize the full-reference IQA framework to compute the quality. Specifically, we apply four types and five levels of distortion aggravation to deal with the commonly encountered distortions. Local binary pattern features are extracted to describe the similarities between the distorted image and the MPRIs. The similarity scores are then utilized to estimate the overall quality. More similar to a specific pseudo reference image (PRI) indicates closer quality to this PRI. Owning to the availability of the created multiple PRIs, we can reduce the influence of image content, and infer the image quality more accurately and consistently. Validation is conducted on four mainstream natural scene image and screen content image quality assessment databases, and the proposed method is comparable to or outperforms the state-of-the-art blind IQA measures. The MATLAB source code of the proposed measure will be publicly available.

Traditional full-reference image quality assessment (IQA) metrics generally predict the quality of the distorted image by measuring its deviation from a perfect quality image called reference image. When the reference image is not fully available, the reduced-reference and no-reference IQA metrics may still be able to derive some characteristics of the perfect quality images, and then measure the distorted image's deviation from these characteristics. In this paper, contrary to the conventional IQA metrics, we utilize a new “reference” called pseudo-reference image (PRI) and a PRI-based blind IQA (BIQA) framework. Different from a traditional reference image, which is assumed to have a perfect quality, PRI is generated from the distorted image and is assumed to suffer from the severest distortion for a given application. Based on the PRI-based BIQA framework, we develop distortion-specific metrics to estimate blockiness, sharpness, and noisiness. The PRI-based metrics calculate the similarity between the distorted image's and the PRI's structures. An image suffering from severer distortion has a higher degree of similarity with the corresponding PRI. Through a two-stage quality regression after a distortion identification framework, we then integrate the PRI-based distortion-specific metrics into a general-purpose BIQA method named blind PRI-based (BPRI) metric. The BPRI metric is opinion-unaware (OU) and almost training-free except for the distortion identification process. Comparative studies on five large IQA databases show that the proposed BPRI model is comparable to the state-of-the-art opinion-aware- and OU-BIQA models. Furthermore, BPRI not only performs well on natural scene images, but also is applicable to screen content images. The MATLAB source code of BPRI and other PRI-based distortion-specific metrics will be publicly available.

In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g., visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images, which are generally thought to be of the best quality. In this paper, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measure of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image data sets. The results of experiments on nine data sets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-reference, reduced-reference and NA IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images, and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications.

The general purpose of seeing a picture is to attain information as much as possible. With it, we in this paper devise a new no-reference/blind metric for image quality assessment (IQA) of contrast distortion. For local details, we lirst roughly remove predicted regions in an image since unpredicted remains are of much information. We then compute entropy of particular unpredicted areas of maximum information via visual saliency. From global perspective, we compare the image histogram with the uniformly distributed histogram of maximum information via the symmetric Kullback-Leibler divergence. The proposed blind IQA method generates an overall quality estimation of a contrast-distorted image by properly combining local and global considerations. Thorough experiments on live databases/subsets demonstrate the superiority of our training-free blind technique over state-of-the-art fulland no-reference IQA methods. Furthermore, the proposed model is also applied to amend the performance of general-purpose blind quality metrics to a sizable margin.

Digital images in the real world are created by a variety of means and have diverse properties. A photographical natural scene image (NSI) may exhibit substantially different characteristics from a computer graphic image (CGI) or a screen content image (SCI). This casts major challenges to objective image quality assessment, for which existing approaches lack effective mechanisms to capture such content type variations, and thus are difficult to generalize from one type to another. To tackle this problem, we first construct a cross-content-type (CCT) database, which contains 1,320 distorted NSIs, CGIs, and SCIs, compressed using the high efficiency video coding (HEVC) intra coding method and the screen content compression (SCC) extension of HEVC. We then carry out a subjective experiment on the database in a well-controlled laboratory environment. Moreover, we propose a unified content-type adaptive (UCA) blind image quality assessment model that is applicable across content types. A key step in UCA is to incorporate the variations of human perceptual characteristics in viewing different content types through a multi-scale weighting framework. This leads to superior performance on the constructed CCT database. UCA is training-free, implying strong generalizability. To verify this, we test UCA on other databases containing JPEG, MPEG-2, H.264, and HEVC compressed images/videos, and observe that it consistently achieves competitive performance.

In this paper, we propose a new no-reference (NR)/ blind sharpness metric in the autoregressive (AR) parameter space. Our model is established via the analysis of AR model parameters, first calculating the energy- and contrast-differences in the locally estimated AR coefficients in a pointwise way, and then quantifying the image sharpness with percentile pooling to predict the overall score. In addition to the luminance domain, we further consider the inevitable effect of color information on visual perception to sharpness and thereby extend the above model to the widely used YIQ color space. Validation of our technique is conducted on the subsets with blurring artifacts from four large-scale image databases (LIVE, TID2008, CSIQ, and TID2013). Experimental results confirm the superiority and efficiency of our method over existing NR algorithms, the state-of-the-art blind sharpness/blurriness estimators, and classical full-reference quality evaluators. Furthermore, the proposed metric can be also extended to stereoscopic images based on binocular rivalry, and attains remarkably high performance on LIVE3D-I and LIVE3D-II databases.