Ranking Consistent Rate: New evaluation criterion on pairwise subjective experiments

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.

Objective. To explore whether the image noises and the metal artifacts could be further managed by the combined use of two technologies, the adaptive statistical iterative reconstruction (ASIR) and the monochromatic imaging generated by gemstone spectral imaging (GSI) dual-energy CT. Materials and Methods. Fifty-one patients with 318 spinal pedicle screws were prospectively scanned with dual energy CT by using fast kV-switching GSI between 80 and 140 kVp. The monochromatic GSI images at 110 keV were reconstructed either without ASIR or with ASIR of various levels (30%, 50%, 70% and 100%). For these five sets of images, both objective and subjective image quality assessments were performed to evaluate the image quality. Results. With objective image quality assessment, the metal artifacts (measured by an artifacts index) significantly decreased when increasing levels of ASIR was utilized (p < 0.001). Moreover, adding ASIR to GSI also decreased the image noise (p < 0.001) and improved the signal-to-noise ratio (SNR, p < 0.001). With subjective image quality analysis, the inter-reader agreements were good, with intra-class correlation coefficients (ICC) of 0.89 to 0.99. Meanwhile, the visualization of the peri-implant soft tissue was improved at higher ASIR levels (p < 0.001). Conclusion. Combined use of ASIR and GSI is shown to decrease the image noise and improve the image quality in post-spinal fusion CT scans. Optimal results were achieved with ASIR levels of over 70%.

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.

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.

MDID: A multiply distorted image database for image quality assessment

Programmable electronic systems and safety guidelines

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.

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.

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.

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.

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.

The demand of an accurate objective image quality assessment tool is important in modern multimedia systems. Image coding algorithms introduce highly structured coding artifacts and distortions. In this paper, we present a novel approach to predict the perceived image quality. Properties of the Human Visual System (HVS) were exploited to select a set of suitable metrics. These metrics are extracted while comparing the reference and distorted image. Mutual Information (MI) and Principal Component Analysis (PCA) were used to obtain an optimal set of objective features that best describe the perceived image quality in respect to subjective scores from human observers. The impairment feature vector is forwarded to the Artificial Neural Network (ANN) where the features are combined and the predicted quality score is computed. Parameters of the ANN are adjusted using Mean Opinion Scores (MOS) obtained from the group of assessors. It is shown that the proposed image quality assessment model can achieve high correlation with the subjective image quality ratings. Possible incorporation of the model into a perceptual image-coding algorithm is proposed. Such a system is capable to ensure that only visually important information is encoded and consequently that the required communication bandwidth is minimized.

Learning-based Image Quality Assessment (IQA) models have obtained impressive performance with the help of reliable subjective quality labels, where Mean Opinion Score (MOS) is the most popular choice. However, in view of the subjective bias of individual annotators, the Labor-Abundant MOS (LA-MOS) typically requires large collections of opinion scores from multiple annotators for each image, which significantly increases the learning cost. In this paper, we aim to learn robust IQA models from Low-Cost MOS (LC-MOS), which only requires very few opinion scores or even a single opinion score for each image. More specifically, we consider the LC-MOS as the noisy observation of LA-MOS and enforce the IQA model learned from LC-MOS to approach the unbiased estimation of LA-MOS. Thus, we represent the subjective bias between LC-MOS and LA-MOS, and the model bias between IQA predictions learned from LC-MOS and LA-MOS (i.e., dual-bias) as two latent variables with unknown parameters. By means of the expectation-maximization-based alternating optimization, we can jointly estimate the parameters of the dual-bias, which suppresses the misleading of LC-MOS via a gated dual-bias calibration (GDBC) module. To the best of our knowledge, this is the first exploration of robust IQA model learning from noisy low-cost labels. Theoretical analysis and extensive experiments on four popular IQA datasets show that the proposed method is robust toward different bias rates and annotation numbers and significantly outperforms the other Learning-based IQA models when only LC-MOS is available. Furthermore, we also achieve comparable performance with respect to the other models learned with LA-MOS.

In previous quality assessment studies on stereoscopic 3D (S3D) images, researchers have concentrated on deriving manually extracted features which represent the quality of images. These features are based on the human visual system or natural scene statistics, but they have not been revealed as a deterministic function, preventing to guarantee the robustness of features. To solve this problem, we introduce a deep learning method for predicting the quality of S3D images without a reference. A convolutional neural network (CNN) model is trained through two-step learning. First, to overcome the lack of training data, patch-based CNNs are introduced. And then, automatically extracted patch features are pooled into image features. Finally, the trained CNN model parameters are updated iteratively using holistic image labeling, i.e., mean opinion score (MOS). The proposed method represents a significant improvement compared to other no-reference (NR) S3D image quality assessment (IQA) algorithms.

The necessity of a substantial dataset for training image quality assessment (IQA) models complicates their learning process, hindering generalizability. Under the differential mean opinion score (DMOS) evaluation standard, we propose an IQA model employing convolutional neural networks (CNNs) for feature extraction, particularly focusing on the intricate relationship between an image's complete response and its distortion information. Our model employs U-Net to extract distortion details, followed by CNN for feature extraction and attention detection on both distorted and undistorted information. This process yields local quality scores and attention weights, contributing to the overall image quality score through weighted summation. By prioritizing the extraction of image distortion information, our model reduces interference from redundant data, thus simplifying the training complexity. To further streamline training, we introduce the concept of \emph{priori distortion scale} (PDS) as an estimation parameter for U-Net iteration steps, eliminating the need for additional parameters. Evaluation on five datasets, including synthetic and real distortions, demonstrates the proposed model's superior performance compared to state-of-the-art IQA algorithms across various metrics. The comprehensive experimental results validate the efficacy of our approach.