Microarchitectural analysis of image quality assessment algorithms

Image Quality Assessment (IQA) algorithms evaluate the perceptual quality of an image using evaluation scores that assess the similarity or difference between two images. We propose a new low-level feature based IQA technique, which applies filter-bank decomposition and center-surround methodology. Differing from existing methods, our model incorporates color intensity adaptation and frequency scaling optimization at each filter-bank level and spatial orientation to extract and enhance perceptually significant features. Our computational model exploits the concept of object detection and encapsulates characteristics proposed in other IQA algorithms in a unified architecture. We also propose a systematic approach to review the evolution of IQA algorithms using unbiased test datasets, instead of looking at individual scores in isolation. Experimental results demonstrate the feasibility of our approach.

With the rise in research on applications of medical image processing, the evaluation of parameters and techniques required for measurement of medical image quality is the need of the hour. The effective, yet automatic methods for measurement of quality of a medical image are of particular interest. This chapter is an overview of different medical imaging technologies, and the related image quality assessment (IQA) algorithms. The main focus is on objective assessment (OA), rather than subjective assessment (SA). Three types of OA-based IQA algorithms are presented in detail: full reference-based IQA (FR-IQA) algorithms; no reference-based IQA (NR-IQA) algorithms and reduced reference-based IQA (RR-IQA) algorithms.

We present a no-reference (NR) image quality assessment (IQA) algorithm that is inspired by the representation of visual scenes in the primary visual cortex of the human visual system. Specifically, we use the sparse coding model of the area V1 to construct an overcomplete dictionary for sparsely representing pristine (undistorted) natural images. First, we empirically demonstrate that the distribution of the sparse representation coefficients of natural images have sharp peaks and heavy tails, and can therefore be modeled using a Univariate Generalized Gaussian Distribution (UGGD). We then show that the UGGD model parameters form good features for distortion estimation and formulate our no-reference IQA algorithm based on this observation. Subsequently, we find UGGD model parameters that are representative of the class of pristine natural images. This is achieved using a training set of undistorted natural images. The perceptual quality of a test image is then defined to be the likelihood of its sparse coefficients being generated from the pristine UGGD model. We show that the proposed algorithm correlates well with subjective evaluation over several standard image databases. Further, the proposed method allows us to construct a distortion map that has several useful applications like distortion localization, adaptive rate allocation etc. Finally and importantly, the proposed NR-IQA algorithm does not make use of any distortion information or subjective scores during the training process.

MDID: A multiply distorted image database for image quality assessment

Programmable electronic systems and safety guidelines

It is a straightforward task for human observers to judge the relative quality of two visual signals of the same content, but subject to different type/level of distortions. However, this comparative image quality assessment (C-IQA) problem remains a difficult challenge for the current research of image quality assessment (IQA). In this paper, we propose a C-IQA approach to predict the relative perceptual quality of a pair of images that are possibly subject to different artifact types/levels. The C-IQA algorithm is designed to emulate the process of comparing the relative quality of two visual stimuli as performed by the human visual system (HVS) within the framework of free energy minimization. The brain's internal generative models initialized on the inputs are used to explain the two images. And their relative quality can then be determined through comparing the free energy level of this model-data fitting process. In the existing work of IQA, the full-reference (FR) and reduced-reference (RR) methods need the prior knowledge of the original images while the no-reference (NR) algorithms usually work with a single input image. The C-IQA approach is inherently different from those existing methods in that it takes as input an image pair and predicts their relative quality without using any knowledge about the original image. A computationally efficient solution to the proposed C-IQA scheme based on a linear autoregressive image model is also introduced. Experimental results show that the proposed method achieves about 98% accuracy in line with the subjective ratings when applied on over 300,000 image pairs sampled from the LIVE database, outperforming the FR metrics such as PSNR, SSIM, and some of the most advanced NR IQA algorithms.

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "full-reference" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by signal fidelity measures. In this paper, we approach the image QA problem as an information fidelity problem. Specifically, we propose to quantify the loss of image information to the distortion process and explore the relationship between image information and visual quality. QA systems are invariably involved with judging the visual quality of "natural" images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of such natural signals. Using these models, we previously presented an information fidelity criterion for image QA that related image quality with the amount of information shared between a reference and a distorted image. In this paper, we propose an image information measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image. Combining these two quantities, we propose a visual information fidelity measure for image QA. We validate the performance of our algorithm with an extensive subjective study involving 779 images and show that our method outperforms recent state-of-the-art image QA algorithms by a sizeable margin in our simulations. The code and the data from the subjective study are available at the LIVE website.

This research aims at providing means to image comparison from different image processing algorithms for performance assessment purposes. Reconstruction of images corrupted by blur and noise requires specialized filtering techniques. Due to the immense effect of these corruptive parameters, it is often impossible to evaluate the quality of a reconstructed image produced by one technique versus another. The algorithm presented here is capable of performing this comparison analytically and quantitatively at a low computational cost (real-time) and high efficiency. The parameters used for comparison are the degree of blurriness, information content, and the amount of various types of noise associated with the reconstructed image. Based on a heuristic analysis of these parameters the algorithm assesses the reconstructed image and quantify the quality of the image by characterizing important aspects of visual quality. Extensive effort has been set forth to obtain real-world noise and blur conditions so that the various test cases presented here could justify the validity of this approach well. The tests performed on the database of images produced valid results for the algorithms consistently. This paper presents the description and validation (along with test results) of the proposed algorithm for blind image quality assessment. DOI: http://dx.doi.org/10.11591/ijece.v2i1.112 Full Text: PDF

Deep feature importance awareness based no-reference image quality prediction

The ubiquity of digital images getting captured and viewed everyday in today's world continuously increases the importance of image processing or image quality assessment to be precise. In this paper, we present an algorithm for Non-Referral Image Quality Assessment (NR-IQA). We make use of the information available at various levels of contourlet transform of a degraded image to predict the level of distortion. We use the simple statistical values of the contourlet coefficients of images to train Neural Network Model which gives us the level of degradation present in the corresponding image. This variable extraction and training algorithm has been found to show high correlation with standard datasets. Benchmarking and validation on the TID2013 dataset indicates that the proposed algorithm outperforms the state of the art algorithms.

Subjective experimental results are widely used as the ground truth in objective Image Quality Assessment (IQA). Specifically, Pairwise Comparison method has superiority over Mean Opinion Scores (MOS), but there is a problem when measuring the consistency between subjective pairwise comparisons and objective quality predictions. In this paper, we first analyze the existing problem of current evaluation method for the consistency between the pairwise comparisons given by human subjects and the ranking results given by objective IQA algorithms. Then we propose a new direct evaluation method, Ranking Consistent Rate, to solve this problem. Moreover, through our method, we can check the self-consistency of datasets based on pairwise comparisons and evaluate the performance of an IQA algorithm more accurately.

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future.

With the rapid development of Internet technology, images on the Internet are used in various aspects of people’s lives. The security and authorization of images are strongly dependent on image quality. Some potential problems have also emerged, among which the quality assessment and denoising of images are particularly evident. This paper proposes a novel NR-IQA method based on the dual convolutional neural network structure, which combines saliency detection with the human visual system (HSV), used as a weighting function to reflect the important distortion caused by the local area. The model is trained using gray and color features in the HSV space. It is applied to the parameter selection of an image denoising algorithm. The experiment proves that our proposed method can accurately evaluate image quality in the process of denoising. It provides great help in parameter optimization iteration and improves the performance of the algorithm. Through experiments, we obtain both improved image quality and a reasonable result of subject assessment when the cascaded algorithm is applied in image security and authorization.

This study focuses on improving the objective alignment of image quality assessment (IQA) algorithms with human visual perception. Existing methodologies, predominantly those based on the Laplacian of Gaussian (LoG) filter, often neglect the impact of color channels on human visual perception. Consequently, we propose a full-reference IQA method that integrates multi-channel visual information in color images. The methodology begins with converting red, green, blue (RGB) images into the luminance (L), red–green opponent color channel (M), blue–yellow opponent color channel (N) or LMN color space. Subsequently, the LoG filter is separately applied to the L, M, and N channels. The convoluted components are then fused to generate a contrast similarity map using the root-mean-square method, while the chromaticity similarity map is derived from the color channels. Finally, multi-channel LoG filtering, contrast, and chromaticity image features are connected. The standard deviation method is then used for sum pooling to create a full-reference IQA computational method. To validate the proposed method, distorted images from four widely used image databases were tested. The evaluation, based on four criteria, focused on the method’s prediction accuracy, computational complexity, and generalizability. The Pearson linear correlation coefficient (PLCC) values, recorded from the databases, ranged from 0.8822 (TID2013) to 0.9754 (LIVE). Similarly, the Spearman rank-order correlation coefficient (SROCC) values spanned from 0.8606 (TID2013) to 0.9798 (LIVE). In comparison to existing methods, the proposed IQA method exhibited superior visual correlation prediction accuracy, indicating its promising potential in the field of image quality assessment.

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.