MDID: A multiply distorted image database for image quality assessment

Programmable electronic systems and safety guidelines

영상 처리 및 컴퓨터 비전 분야에 있어서, 평균 제곱 오차(Mean Squared Error: MSE)는 좋은 수학적 특성(예를 들어, 척도성(metricability), 미분가능성(differentiability) 및 볼록 성질(convexity))을 가짐으로 인해 많은 영상 화질 최적화 문제의 객관적 척도로 사용되어 왔다. 그러나 MSE가 영상의 왜곡 신호에 대한 시각적 인지 화질과 상관도가 높지 않다는 것이 알려지면서, 이를 해결하기 위해 위에서 언급한 좋은 수학적 특성과 높은 영상 화질 예측 성능을 동시에 가지는 객관적 영상 화질 측정(Image Quality Assessment: IQA)척도가 활발히 연구되어 왔다. 비록 최근 제안된 좋은 수학적 성질을 만족시키는 IQA 척도들은 MSE와 비교하여 매우 향상된 주관적 화질 예측 성능을 보이지만, 상대적으로 높은 계산 복잡도를 가진다. 본 논문은 이를 해결하기 위해, 단순 라플라스 연산자를 이용한 좋은 수학적 특성을 가지는 새로운 IQA 척도를 제안한다. 제안 IQA 방법에 도입한 단순 라플라스 연산자는 인간 시각 체계의 망막에서의 광도 자극에 대한 시신경 반응을 효과적으로 모사할 뿐만 아니라 계산이 매우 단순하기 때문에, 제안 IQA 척도는 단순 라플라스 연산자를 사용하여 매우 빠른 계산 속도와 높은 주관적 화질 점수 예측력을 확보하였다. 제안 IQA 척도의 효과를 검증하기 위해, 최신 IQA 척도들과 광범위한 성능비교 실험을 수행하였다. 실험 결과, 제안하는 IQA 척도는 모든 테스트 IQA 척도들 중 MSE를 제외하고 가장 빠른 처리 속도를 보였을 뿐만 아니라, 가장 높은 주관적 화질예측 성능을 보였다. In image processing and computer vision fields, mean squared error (MSE) has popularly been used as an objective metric in image quality optimization problems due to its desirable mathematical properties such as metricability, differentiability and convexity. However, as known that MSE is not highly correlated with perceived visual quality, much effort has been made to develop new image quality assessment (IQA) metrics having both the desirable mathematical properties aforementioned and high prediction performances for subjective visual quality scores. Although recent IQA metrics having the desirable mathematical properties have shown to give some promising results in prediction performance for visual quality scores, they also have high computation complexities. In order to alleviate this problem, we propose a new fast IQA metric using a simple Laplace operator. Since the Laplace operator used in our IQA metric can not only effectively mimic operations of receptive fields in retina for luminance stimulus but also be simply computed, our IQA metric can yield both very fast processing speed and high prediction performance. In order to verify the effectiveness of the proposed IQA metric, our method is compared to some state-of-the-art IQA metrics. The experimental results showed that the proposed IQA metric has the fastest running speed compared the IQA methods except MSE under comparison. Moreover, our IQA metric achieves the best prediction performance for subjective image quality scores among the state-of-the-art IQA metrics under test.

Image and video compression aims at finding an optimal trade-off between rate and distortion. This is done through Rate-Distortion Optimization (RDO) in traditional en-coders with the use of Image Quality Assessment (IQA) metrics. While it is known that most IQA metrics are designed to be correlated with human perception, there is no evidence that this observation can be generalized in a Video Coding for Machines (VCM) context, where the receiver is not a human anymore but a machine. In this paper, we propose an evaluation protocol to measure the correlation level between conventional Full-Reference (FR) IQA metrics and machine perception through the semantic segmentation vision task. Experiments showed a relatively low correlation between them when measured on the block-level. This observation implies the need of RDO algorithms that are better suited for Machine-to-Machine (M2M) communications. In order to facilitate the emergence of IQA metrics that better reflect machine perception, the code and dataset used to perform this study is made freely available at https://github.com/albmarie/iqa_m2m_segmentation.

Recently, various objective image quality assessment (IQA) metrics have been proposed for measuring the quality of a captured image. Moreover, IQA databases have been made available to enable researchers develop and test IQA metrics. For instance, the TID2013 database was used in the experiments conducted in this study. It contains many test images obtained from 25 reference images, each having 24 types of distortion and five levels of noise for each distortion. However, estimating a subjective evaluation score for many types of noise is a difficult task. Therefore, this study proposes an objective IQA framework for evaluating noise clusters using image features. Experimental results confirmed the effectiveness of the proposed framework.

In this study, the combining strategies are considered to be effective tools to improve the performance of the image quality assessment (IQA) metrics. A new metric is proposed to evaluate the quality of test images of combined degradation and individual degradation. The complex numbers are used to describe the image structure in the proposed method. On the basis of that, the properties of the classical IQA method based on singular value decomposition are analysed. The difference between energy visual map and structure visual map is shown. The complex‐number‐based approach is different from the classical scalar‐based techniques, which are insufficient to describe image structure. The proposed C_SVDQ metric can be considered as a vectorial expansion of structure similarity. In the experiments, an extensive comparison between the proposed C_SVDQ and other IQA metrics on image quality database was performed. Both the overall tests and the individual distortion tests show the superiority of this new approach in IQA.

Artificial Intelligence (AI) is the enhancement and method of computer system that handles tasks which requires human like intelligence such as recognition, language translation and visual interpretation. Subjective image quality assessment (IQA) is difficult to be implemented in real-time systems, methodology for enhancing the involvement in producing IQA model is to improve the quality of image by significant evaluation. Intuitively, human eyes are not sensitive to the distortion and damage from the area with lesser visual saliency (VS), VS is closely related to IQA. With this consideration, an effective IQA was proposed, which involved two processes. The local quality map of a distorted image was computed using the structural similarity function of its feature attributes, such as brightness, chrominance and gradient. Second, the local quality map was weighted with visual saliency (VS) to get the objective evaluation of image quality. The VS was modeled by extracting the saliency of low-level features of the image, wiping off the molestation information from these saliency based on an apriori threshold, and combining the effective information to construct the saliency map. Image processing using fuzzy is gathering features and segments as fuzzy set while processing images. The experiments on the two largest database for six classical IQA metrics demonstrate that performance of weighted-VS IQA metrics is superior to the performance of no weighted-VS IQA metrics, and the proposed IQA method has higher computational accuracy than the other IQA metrics under a moderate computational complexity, especially for two types of distortion images, such as local block-wise (Block) and fast-fading (FTF).

Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples. In this study, a No-Reference IQA (NR-IQA), wood NR-IQA (WNR-IQA) metric was proposed to assess the quality of wood images. Support Vector Regression (SVR) was trained using Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) features, which were measured for wood images. Meanwhile, the Mean Opinion Score (MOS) was obtained from the subjective evaluation. This was followed by a comparison between the proposed IQA metric, WNR-IQA, and three established NR-IQA metrics, namely Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), deepIQA, Deep Bilinear Convolutional Neural Networks (DB-CNN), and five Full Reference-IQA (FR-IQA) metrics known as MSSIM, SSIM, FSIM, IWSSIM, and GMSD. The proposed WNR-IQA metric, BRISQUE, deepIQA, DB-CNN, and FR-IQAs were then compared with MOS values to evaluate the performance of the automatic IQA metrics. As a result, the WNR-IQA metric exhibited a higher performance compared to BRISQUE, deepIQA, DB-CNN, and FR-IQA metrics. Highest quality images may not be routinely available due to logistic factors, such as dust, poor illumination, and hot environment present in the timber industry. Moreover, motion blur could occur due to the relative motion between the camera and the wood slice. Therefore, the advantage of WNR-IQA could be seen from its independency from a “perfect” reference image for the image quality evaluation.

Image quality assessment (IQA) is critically important for the image-processing field. IQA aims to build a computational model to predict human perceived image quality, accurately and automatically. Until now, great efforts have been employed to design IQA metrics. In this paper, we systematically and comprehensively review the fundamental, brief history, and state-of-the-art developments of IQA, with emphasis on natural image quality assessment (NIQA). First, the definition of image quality is discussed, which contains three aspects and lead to different philosophies of designing IQA metrics. Afterwards, classic NIQA metrics are presented with some further discussions. Widely used databases and the performances of classic NIQA metrics on them are also listed. We highlight the most significant works and some open issues about the developments of IQA, and provide the benchmarks for the researchers and scholars who work on IQA.

This paper describes an image quality assessment (IQA) metric based on the visual perception of image contents (VPIC)). In the metric, VPIC is firstly modelled by simulating the nonlinearity of luminance perception, masking properties and contrast sensitivity characteristics of human visual system (HVS). Then the source and distorted images are processed by this model respectively, and their intensity differences are calculated. Finally, based on the intensity differences, an IQA model is built. And 47 reference images and 1549 distorted images in the LIVE, TID2008 and CSIQ databases are tested with the IQA metric. The results show that it is helpful to improve the consistency between the objective IQA scores and the subjective mean opinion scores (MOSs) combining the visual perception and complexity of image contents.

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.

Algorithms for image quality assessment (IQA) aim to predict the qualities of images in a manner that agrees with subjective quality ratings. Over the last several decades, the major impetus in IQA research has focused on improving predictive performance; very few studies have focused on analyzing and improving the runtime performance of IQA algorithms. This paper is the first to examine IQA algorithms from the perspective of their interaction with the underlying hardware and microarchitectural resources, and to perform a systematic performance analysis using state-of-the-art tools and techniques from other computing disciplines. We implemented four popular full-reference IQA algorithms (most apparent distortion, multiscale structural similarity, visual information fidelity, and visual signal-to-noise ratio) and two no-reference algorithms (blind image integrity notator using DCT statistics and blind/referenceless image spatial quality evaluator) in C++ based on the code provided by their respective authors. We then conducted a hotspot analysis to identify sections of code that were performance bottlenecks and performed microarchitectural analysis to identify the underlying causes for these bottlenecks. Despite the fact that all six algorithms share common algorithmic operations (e.g., filterbanks and statistical computations), our results revealed that different IQA algorithms overwhelm different microarchitectural resources and give rise to different types of bottlenecks. Based on these results, we propose microarchitectural-conscious coding techniques and custom hardware recommendations for performance improvement.

In this paper some possibilities of using the GPGPU programming techniques for a fast estimation of the recently proposed combined video quality metric are discussed. Such metric consists of three state-of-the-art image quality assessment metrics applied using frame-by-frame analysis with appropriate weighting coefficients. Since this combined metric is better correlated with subjective quality scores than each of its components, especially for the contaminations typical for the wireless transmission of compressed video data, the next step is related to its efficient implementation useful for real-time applications. In the paper an efficient implementation of the estimated combined metric is presented together with the verification of its linear correlation with subjective video quality evaluations performed using the LIVE Wireless Video Quality Assessment Database containing 160 video files with four types of distortions and their Differential Mean Opinion Score (DMOS) values.

View synthesis brings geometric distortions which are not handled efficiently by existing image quality assessment metrics. Despite the widespread of 3-D technology and notably 3D television (3DTV) and free-viewpoints television (FTV), the field of view synthesis quality assessment has not yet been widely investigated and new quality metrics are required. In this study, we propose a new full-reference objective quality assessment metric: the View Synthesis Quality Assessment (VSQA) metric. Our method is dedicated to artifacts detection in synthesized view-points and aims to handle areas where disparity estimation may fail: thin objects, object borders, transparency, variations of illumination or color differences between left and right views, periodic objects... The key feature of the proposed method is the use of three visibility maps which characterize complexity in terms of textures, diversity of gradient orientations and presence of high contrast. Moreover, the VSQA metric can be defined as an extension of any existing 2D image quality assessment metric. Experimental tests have shown the effectiveness of the proposed method.

Image quality assessment (IQA) plays a crucial role in monitoring quality control in image communication systems, and in benchmarking and optimizing parameters in enhancement algorithms. The full-reference IQA metrics require a good-quality reference image, obtaining which may not be practical in real-life applications. This paper, therefore, proposes a no-reference IQA metric based on the hypothesis that every image has latent additive white Gaussian noise (AWGN). A mathematical model was developed on a dataset of fifty test images by computing gradient-based structural similarity of corrupted images w.r.t. the original. Statistical modeling of the observations were found to fit an exponential parametric model. The standard deviation of the latent (or apparent) AWGN present in any image was estimated using an SVD-based approach. The proposed metric, referred to as the no-reference gradient-based structural integrity (NRGSI), is then computed by a simple backprojection of the estimated noise deviation on the exponential model. The accuracy of the proposed objective metric is characterized by its comparison with subjective quality scores given by ten subjects, and with a classical perceptual quality measure.

In this paper, a novel no-reference image visual quality metric is proposed based on fusion of statistical and human visual system based metrics using ε-Support Vector Regression. Different order polynomial regression was also examined as an approximation that has lower computational complexity. Compared to existing image quality assessment metrics, the proposed fused metric is able to better quantify the image quality regardless of the type of degradation. We furthermore improve the image quality assessment by training a separate regression model for each degradation type. The latter degradation specific approach yields near perfect correlation with subjective scores, however, it relies on prior knowledge of the degradation process.