General-purpose No-reference Image Quality Assessment Research Articles

No-Reference Image Quality Assessment Based on Edge Pattern Feature in the Spatial Domain

This paper proposes a general-purpose no-reference image quality assessment (NR-IQA) method that investigates the image’s structure information from a new aspect, i.e., the characteristic of image edge profiles that depict the directional property of adjacent edge points in the spatial domain of the image. More specifically, we extracted the image’s edge map based on Laplacian of Gaussian (LoG) filtration and zero-crossing (ZC) detection and refined the edge map to be 1-pixel wide. We then explored the edge map by investigating edge profiles’ statistics in a local window with a $5\times 5$ -pixel size. Considering the consensus that natural images consist of directional structures, we found that the spatial distribution property of adjacent edge points can be represented through several edge profiles called edge patterns, which are selected from natural images with a proposed smooth criterion. With the proposed edge patterns and their statistical histogram for the image and the support vector regression technique, we proposed the NR-IQA model based on the edge patterns in the spatial domain, named EPISD. The proposed method has been extensively validated on the LIVE, CSIQ, TID2013, MDID2017, SIQAD, and SCID databases. The experimental results showed that EPISD has a competitive performance with state-of-the-art methods and works stably across different databases.

No‐reference image quality metric based on multiple deep belief networks

The last decade has witnessed great advances in digital images. These images are subjected to many processing stages during storing, transmitting, or sharing over a network connection. Unfortunately, these processing stages could potentially add visual degradation to original image. These degradations reduce the perceived visual quality which leads to an unsatisfactory experience for human viewers. Therefore, image quality assessment (IQA) has become a topic of high interest and intense research over the last decade. This study mainly focuses on the most challenging category of IQA general-purpose No-Reference Image Quality Assessment (NR-IQA), where the goal is to assess the quality of images without information about the reference images and without prior knowledge about the types of distortions in the tested image. A novel NR-IQA approach is presented, by utilizing multiple deep belief networks (DBNs) with multiple regression models. It consists of four DBNs. Each DBN is associated with one type of distortion. The authors have evaluated the performance of the proposed and some existing models on a fair basis. The obtained results show that their model gives better results and yield a significant improvement.

Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?

Image content variation is a typical and challenging problem in no-reference image-quality assessment (NR-IQA). This work pays special attention to the impact of image content variation on NR-IQA methods. To better analyze this impact, we focus on blur-dominated distortions to exclude the impacts of distortion-type variations. We empirically show that current NR-IQA methods are inconsistent with human visual perception when predicting the relative quality of image pairs with different image contents. In view of deep semantic features of pretrained image classification neural networks always containing discriminative image content information, we put forward a new NR-IQA method based on semantic feature aggregation (SFA) to alleviate the impact of image content variation. Specifically, instead of resizing the image, we first crop multiple overlapping patches over the entire distorted image to avoid introducing geometric deformations. Then, according to an adaptive layer selection procedure, we extract deep semantic features by leveraging the power of a pretrained image classification model for its inherent content-aware property. After that, the local patch features are aggregated using several statistical structures. Finally, a linear regression model is trained for mapping the aggregated global features to image-quality scores. The proposed method, SFA, is compared with nine representative blur-specific NR-IQA methods, two general-purpose NR-IQA methods, and two extra full-reference IQA methods on Gaussian blur images (with and without Gaussian noise/JPEG compression) and realistic blur images from multiple databases, including LIVE, TID2008, TID2013, MLIVE1, MLIVE2, BID, and CLIVE. Experimental results show that SFA is superior to the state-of-the-art NR methods on all seven databases. It is also verified that deep semantic features play a crucial role in addressing image content variation, and this provides a new perspective for NR-IQA.

Multitask convolutional neural network for no-reference image quality assessment

We propose a multitask convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA). We decompose the task of rating image quality into two subtasks, namely distortion identification and distortion-level estimation, and then combine the results of the two subtasks to obtain a final image quality score. Unlike conventional multitask convolutional networks, wherein only the early layers are shared and the subsequent layers are different for each subtask, our model shares almost all the layers by integrating a dictionary into the CNN. Moreover, it is trained in an end-to-end manner, and all the parameters, including the weights of the convolutional layers and the codewords of the dictionary, are simultaneously learned from the loss function. We test our method on widely used image quality databases and show that its performance is comparable with those of state-of-the-art general-purpose NR-IQA algorithms.

No-Reference Image Quality Assessment Using Orthogonal Color Planes Patterns

This paper proposes a new general-purpose no-reference image quality assessment (NR-IQA) method based on color texture analysis. Specifically, the proposed method uses the statistics of the orthogonal color planes pattern (OCPP) descriptor to characterize image quality. The OCPP descriptor, proposed in this paper, is an extension of the local binary pattern operator that incorporates color information. To make NR-IQA methods more generic, that is, more sensitivity to different types of degradation (e.g., color and contrast degradation), it is important to take into consideration the color information. In the proposed NR-IQA method, we use the statistics of the OCPP descriptor as an input vector to a regression algorithm, which models the nonlinear relationship between the OCPP statistics and the subjective opinion scores. Experimental results show that proposed IQA method is quite efficient when compared to popular state-of-the-art NR-IQA methods.

Deep supervised dictionary learning for no-reference image quality assessment

We propose a deep convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA), i.e., accurate prediction of image quality without a reference image. The proposed model consists of three components such as a local feature extractor that is a fully CNN, an encoding module with an inherent dictionary that aggregates local features to output a fixed-length global quality-aware image representation, and a regression module that maps the representation to an image quality score. Our model can be trained in an end-to-end manner, and all of the parameters, including the weights of the convolutional layers, the dictionary, and the regression weights, are simultaneously learned from the loss function. In addition, the model can predict quality scores for input images of arbitrary sizes in a single step. We tested our method on commonly used image quality databases and showed that its performance is comparable with that of state-of-the-art general-purpose NR-IQA algorithms.

Hybrid NSS features for no‐reference image quality assessment

A novel general-purpose no-reference image quality assessment (NR-IQA) model utilising hybrid natural scene statistics (HNSS) is proposed. Distinguished from existing NR-IQA approaches, the new model combines the statistics of locally mean subtracted and contrast normalised coefficients in the spatial domain and the statistics of image patch coefficients in a codebook space, which is constructed by codebooks extracted from pristine images using K-Means. The authors demonstrate that the coefficients in the codebook space keep the NSS characteristics as same as these in the spatial domain. After extracting the statistical features, a two-stage framework of distortion classification followed by quality assessment is applied. Experimental results show that the authors’ predicted quality score well matches human perceptual image quality. The proposed model outperforms state-of-the-art general-purpose NR-IQA approaches when it is tested on the LIVE database.

Toward a No-Reference Image Quality Assessment Using Statistics of Perceptual Color Descriptors.

Analysis of the statistical properties of natural images has played a vital role in the design of no-reference (NR) image quality assessment (IQA) techniques. In this paper, we propose parametric models describing the general characteristics of chromatic data in natural images. They provide informative cues for quantifying visual discomfort caused by the presence of chromatic image distortions. The established models capture the correlation of chromatic data between spatially adjacent pixels by means of color invariance descriptors. The use of color invariance descriptors is inspired by their relevance to visual perception, since they provide less sensitive descriptions of image scenes against viewing geometry and illumination variations than luminances. In order to approximate the visual quality perception of chromatic distortions, we devise four parametric models derived from invariance descriptors representing independent aspects of color perception: 1) hue; 2) saturation; 3) opponent angle; and 4) spherical angle. The practical utility of the proposed models is examined by deploying them in our new general-purpose NR IQA metric. The metric initially estimates the parameters of the proposed chromatic models from an input image to constitute a collection of quality-aware features (QAF). Thereafter, a machine learning technique is applied to predict visual quality given a set of extracted QAFs. Experimentation performed on large-scale image databases demonstrates that the proposed metric correlates well with the provided subjective ratings of image quality over commonly encountered achromatic and chromatic distortions, indicating that it can be deployed on a wide variety of color image processing problems as a generalized IQA solution.

Perceived no reference image quality measurement for chromatic aberration

Today there is need for no reference (NR) objective perceived image quality measurement techniques as conducting subjective experiments and making reference image available is a very difficult task. Very few NR perceived image quality measurement algorithms are available for color distortions like chromatic aberration (CA), color quantization with dither, and color saturation. We proposed NR image quality assessment (NR-IQA) algorithms for images distorted with CA. CA is mostly observed in images taken with digital cameras, having higher sensor resolution with inexpensive lenses. We compared our metric performance with two state-of-the-art NR blur techniques, one full reference IQA technique and three general-purpose NR-IQA techniques, although they are not tailored for CA. We used a CA dataset in the TID-2013 color image database to evaluate performance. Proposed algorithms give comparable performance with state-of-the-art techniques in terms of performance parameters and outperform them in terms of monotonicity and computational complexity. We have also discovered that the proposed CA algorithm best predicts perceived image quality of images distorted with realistic CA.