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China Communications 2023年第6期20卷 215-228页

作者： Xiaoyu Ma Suiyu Zhang Chang Liu Dingguo Yu Communication University of Zhejiang No.998Xueyuan RoadQiantang DistrictHangzhou 310042ZhejiangChina Peng Cheng Laboratory No.2Xingke RoadNanshan DistrictShenzhen 518055GuangdongChina

Blind image quality assessment(BIQA)is of fundamental importance in low-level computer vision community.Increasing interest has been drawn in exploiting deep neural networks for BIQA.Despite of the notable success achieved,there is a broad consensus that training deep convolutional neural networks(DCNN)heavily relies on massive annotated data.Unfortunately,BIQA is typically a small sample problem,resulting the generalization ability of BIQA severely restricted.In order to improve the accuracy and generalization ability of BIQA metrics,this work proposed a totally opinion-unaware BIQA in which no subjective annotations are involved in the training stage.Multiple full-reference image quality assessment(FR-IQA)metrics are employed to label the distorted image as a substitution of subjective quality annotation.A deep neural network(DNN)is trained to blindly predict the multiple FR-IQA score in absence of corresponding pristine image.In the end,a selfsupervised FR-IQA score aggregator implemented by adversarial auto-encoder pools the predictions of multiple FR-IQA scores into the final quality predicting score.Even though none of subjective scores are involved in the training stage,experimental results indicate that our proposed full reference induced BIQA framework is as competitive as state-of-the-art BIQA metrics.

关键词： deep neural networks image quality assessment adversarial auto encoder

image quality assessment BASED ON CONTOUR AND REGION

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Journal of Computational Mathematics 2016年第6期34卷 705-722页

作者： Chen Huang Ming Jiang Tingting Jiang LMAM School of Mathematical Sciences Beijing International Center for Mathematical Research and Cooperative Medianet Innovation Center Peking University Beijing 100871 China NELVT National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer Science and Cooperative Medianet Innovation Center Peking University Beijing 100871 China

image quality assessment （IQA） is a fundamental problem in image processing. It is a common principle that human vision is hierarchical： we first perceive global structural information such as contours then focus on local regional details if necessary. Following this principle, we propose a novel framework for IQA by quantifying the degenerations of structural information and region content separately, and mapping both to obtain the objective score. The structural information can be obtained as contours by contour detec- tion techniques. Experiments are conducted to demonstrate its performance in comparison with multiple state-of-the-art methods on two large scale datasets.

关键词： image quality assessment Contour detection image segmentation.

CNN Based No-Reference HDR image quality assessment

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Chinese Journal of Electronics 2021年第2期30卷 282-288页

作者： FAN Kefeng LIANG Jiyun LI Fei QIU Puye Digital Technology Research Center China Electronics Standardization Institute School of Electronic Engineering and Automation Guilin University of Electronic Technology Guangzhou Sequoia DB Co.

Motivated by the problems of non-universality and over-reliance on the original reference image in High dynamic range(HDR) image quality assessment(IQA), a convolutional neural network-based algorithm for no-reference HDR image quality assessment is proposed. The Salience detection by self-resemblance(SDSR) algorithm which extracts the salient regions of the HDR image, is used to simulate the human visual attention mechanism. Then a visual quality perception network for training quality prediction models is designed according to the visual characteristics of luminance and contrast sensitivity. And this network consists of an Error estimation network(Error-net), a Perceptual resistance network(PR-net) and a mixing function. The experimental results indicate that the method proposed has high consistency with subjective perception, and the value of assessment metrics Spearman rank-order correlation coefficient(SROCC), Pearson product-moment correlation coefficient(PLCC) and Root mean square error(RMSE)correspondingly reaches 0.941, 0.910 and 8.176 as well. It is comparable with classic full-reference HDR IQA methods.

关键词： High dynamic range image image quality assessment Convolutional neural network Human visual system

A Novel Spatial Pooling Strategy for image quality assessment

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Journal of Computer Science & Technology 2016年第2期31卷 225-234页

作者： Qiaohong Li Yu-Ming Fang Member, CCF Jing-Tao Xu School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing 100086 China

A variety of existing image quality assessment （IQA） metrics share a similar two-stage framework： at the first stage, a quality map is constructed by comparison between local regions of reference and distorted images; at the second stage, the spatial pooling is adopted to obtain overall quality score. In this work, we propose a novel spatial pooling strategy for image quality assessment through statistical analysis of the quality map. Our in-depth analysis indicates that the overall image quality is sensitive to the quality distribution. Based on the analysis, the quality histogram and statistical descriptors extracted from the quality map are used as input to the support vector regression to obtain the final objective quality score. Experimental results on three large public IQA databases have demonstrated that the proposed spatial pooling strategy can greatly improve the quality prediction performance of the original IQA metrics in terms of correlation with human subjective ratings.

关键词： image quality assessment spatial pooling statistical pooling support vector regression structural similarity

image Signature Based Mean Square Error for image quality assessment

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Chinese Journal of Electronics 2015年第4期24卷 755-760页

作者： CUI Ziguan GAN Zongliang TANG Guijin LIU Feng ZHU Xiuchang image Processing and image Communication Laboratory Nanjing University of Posts and Telecommunications

Motivated by the importance of Human visual system(HVS) in image processing, we propose a novel image signature based mean square error(ISMSE)metric for full reference image quality assessment(IQA).Efficient image signature based describer is used to predict visual saliency map of the reference image. The saliency map is incorporated into luminance difference between the reference and distorted images to obtain image quality score. The effect of luminance difference on visual quality with larger saliency value which is usually corresponding to foreground objects is highlighted. Experimental results on LIVE database release 2 show that by integrating the effects of image signature based saliency on luminance difference, the proposed ISMSE metric outperforms several state-of-the-art HVS-based IQA metrics but with lower complexity.

关键词： image quality assessment Human visual system image signature Saliency Mean square error

SWVFS： a saliency weighted visual feature similarity metric for image quality assessment

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Frontiers of Computer Science 2014年第1期8卷 145-155页

作者： Li CUI School of Electronics and Information Northwestern Polytechnical University Xi'an 710072 China

In this paper, a saliency weighted visual feature similarity （SWVFS） metric is proposed for full reference im- age quality assessment （IQA）. Instead of traditional spatial pooling strategies, a visual saliency-based approach is em- ployed for better compliance with properties of the human visual system, where the saliency allocation is closely related to the activity of posterior parietal cortex and the pluvial nu- clei of the thalamus. Assuming that the saliency map actually represents the contribution of locally computed visual distor- tions to the overall image quality, the gradient similarity and the textural congruency are merged into the final image qual- ity indicator. The gradient and texture comparison play com- plementary roles in characterizing the local image distortion. Extensive experiments conducted on seven publicly available image databases show that the performance of SWVFS is competitive with the state-of-the-art IQA algorithms.

关键词： image quality assessment gradient texture vi-sual saliency

Perceptual image quality assessment metric using mutual information of Gabor features

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Science China(Information Sciences) 2014年第3期57卷 130-138页

作者： DING Yong ZHANG Yuan WANG Xiang YAN XiaoLang KRYLOV Andrey S. Institute of VLSI Design Zhejiang University Laboratory of Mathematical Methods of image Processing Lomonosov Moscow State University

A good objective metric of image quality assessment(IQA)should be consistent with the subjective judgment of human beings.In this paper,a four-stage perceptual approach for full reference IQA is presented.In the first stage,the visual features are extracted by 2-D Gabor filter that has the excellent performance of modeling the receptive fields of simple cells in the primary visual cortex.Then in the second stage,the extracted features are post-processed by the divisive normalization transform to reflect the nonlinear mechanisms in human visual systems.In the third stage,mutual information between the visual features of the reference and distorted images is employed to measure the visual quality.And in the last pooling stage,the mutual information is converted to the final objective quality score.Experimental results show that the proposed metic has a high correlation with the subjective assessment and outperforms other state-of-the-art metrics.

关键词： image quality assessment mutual information Gabor features perceptual metric entropy

image and Video quality assessment Using Neural Network and SVM

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Tsinghua Science and Technology 2008年第1期13卷 112-116页

作者：丁文锐佟雨兵张其善杨东凯 School of Electronic and Information Engineering Beihang University

An image and video quality assessment method was developed using neural network and support vector machines （SVM） with the peak signal to noise ratio （PSNR） and the structure similarity indexes used to describe image quality. The neural network was used to obtain the mapping functions between the objective quality assessment indexes and subjective quality assessment. The SVM was used to classify the images into different types which were accessed using different mapping functions. Video quality was assessed based on the quality of each frame in the video sequence with various weights to describe motion and scene changes in the video. The number of isolated points in the correlations of the image and video subjective and objective quality assessments was reduced by this method. Simulation results show that the method accurately accesses image quality. The monotonicity of the method for images is 6.94% higher than with the PSNR method, and the root mean square error is at least 35.90% higher than with the PSNR.

关键词： image quality assessment support vector machines （SVM） neural network isolated points

Blind image quality assessment Based on Hybrid Fuzzy-Genetic Technique

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Journal of Beijing Institute of Technology 2003年第4期12卷 395-398页

作者：王海沈庭芝谢志宏 Department of Electronic Engineering

A new method for no-reference image quality assessment based on hybrid fuzzy-genetic technique is proposed. Noise variance and edge sharpness level of the restored image are two basic metrics for assessing the performance of the restoration algorithm, then a fuzzy if-then inference system is developed to combine the two metrics to get a final quality score, and the parameters of the fuzzy membership function are trained with genetic algorithms. Experiments results show that the image quality score correlates well with mean opinion score and the proposed approach is robust and effective.

关键词： image quality assessment fuzzy inference system genetic algorithms

A METHOD OF image quality assessment FOR COMPRESSIVE SAMPLING VIDEO TRANSMISSION

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Journal of Electronics(China) 2012年第6期29卷 598-603页

作者： Chen Shouning Zheng Baoyu Li Jing Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications

Based on compressive sampling transmission model, we demonstrate here a method of quality evaluation for the reconstruction images, which is promising for the transmission of unstructured signal with reduced dimension. By this method, the auxiliary information of the recovery image quality is obtained as a feedback to control number of measurements from compressive sampling video stream. Therefore, the number of measurements can be easily derived at the condition of the absence of information sparsity, and the recovery image quality is effectively improved. Theoretical and experimental results show that this algorithm can estimate the quality of images effectively and is in well consistency with the traditional objective evaluation algorithm.

关键词： Compressive sampling image quality assessment Measurements feedback