It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural si...It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural similarity based image quality assessment was proposed under the assumption that the Human Visual System(HVS)is highly adapted for extracting structural information from an image.While the demand on high color quality increases in the media industry,color loss will make the visual quality different.In this paper,we proposed an improved quality assessment(QA)method by adding color comparison into the structural similarity(SSIM)measurement system for evaluating color image quality.Then we divided the task of similarity measurement into four comparisons:luminance,contrast,structure,and color.Experimental results show that the predicted quality scores of the proposed method are more effective and consistent with visual quality than the classical methods using five different distortion types of color image sets.展开更多
Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate eval...Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.展开更多
Protein structure Quality Assessment（QA） is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure ...Protein structure Quality Assessment（QA） is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA.In this work, we developed a new Hidden Markov Model（HMM） to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary structures, and sequence profiles. This model has two capabilities：（1） encoding local structure of each position by jointly considering sequence and structure information, and（2）assigning a global score to estimate the overall quality of a predicted structure, as well as local scores to assess the quality of specific regions of a structure, which provides useful guidance for targeted structure refinement. We compared the HMM model to state-of-art single structure quality assessment methods OPUSCA, DFIRE, GOAP,and RW in protein structure selection. Computational results showed our new score HMM.Z can achieve better overall selection performance on the benchmark datasets.展开更多
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 imag...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.展开更多
Agricultural land quality assessment of karst mountain areas is an impor-tant means to protect and manage cultivated land quality of karst mountain areas. With Jingxi County as a case, the data resources included achi...Agricultural land quality assessment of karst mountain areas is an impor-tant means to protect and manage cultivated land quality of karst mountain areas. With Jingxi County as a case, the data resources included achievements of cultivat-ed land grade, achievements of second land investigation, investigation of local con-ditions and the other data resources. With the overlay tool of MapGIS, attribute data of evaluation index were obtained, which can reflect agricultural land＇ characteris-tics, and agricultural land quality scores were calculated and land quality was grad-ed using the model of comprehensive evaluation of multiple factors to make further analysis on characteristics and spatial distribution. The results showed that used agricultural land is dominated by middle and high-grade lands in Jingxi County mainly, representing 68.58% of Jingxi County’s agricultural lands. On the other hand, the quality of paddy land is general y higher than that of dry land, and quality of agricultural land showed higher in middle and eastern Jingxi, and lower in north-western region, which provides references for national land management.展开更多
文摘It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural similarity based image quality assessment was proposed under the assumption that the Human Visual System(HVS)is highly adapted for extracting structural information from an image.While the demand on high color quality increases in the media industry,color loss will make the visual quality different.In this paper,we proposed an improved quality assessment(QA)method by adding color comparison into the structural similarity(SSIM)measurement system for evaluating color image quality.Then we divided the task of similarity measurement into four comparisons:luminance,contrast,structure,and color.Experimental results show that the predicted quality scores of the proposed method are more effective and consistent with visual quality than the classical methods using five different distortion types of color image sets.
基金This work was supported by National Natural Science Foundation of China(Nos.61831015 and 61901260)Key Research and Development Program of China(No.2019YFB1405902).
文摘Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.
基金supported by National Institutes of Health grants R21/R33-GM078601 and R01-GM100701
文摘Protein structure Quality Assessment（QA） is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA.In this work, we developed a new Hidden Markov Model（HMM） to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary structures, and sequence profiles. This model has two capabilities：（1） encoding local structure of each position by jointly considering sequence and structure information, and（2）assigning a global score to estimate the overall quality of a predicted structure, as well as local scores to assess the quality of specific regions of a structure, which provides useful guidance for targeted structure refinement. We compared the HMM model to state-of-art single structure quality assessment methods OPUSCA, DFIRE, GOAP,and RW in protein structure selection. Computational results showed our new score HMM.Z can achieve better overall selection performance on the benchmark datasets.
基金This work was supported by the National Natural Science Foundation of China under Grant No. 61571212 and the Natural Science Foundation of Jiangxi Province of China under Grant No. 20151BDH80003.
文摘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.
基金Supported by Supplemented Project of Guangxi Zhuang Autonomous Region Agricultural Land Quality Grade Achievements(GXKLJ20133073-E)~~
文摘Agricultural land quality assessment of karst mountain areas is an impor-tant means to protect and manage cultivated land quality of karst mountain areas. With Jingxi County as a case, the data resources included achievements of cultivat-ed land grade, achievements of second land investigation, investigation of local con-ditions and the other data resources. With the overlay tool of MapGIS, attribute data of evaluation index were obtained, which can reflect agricultural land＇ characteris-tics, and agricultural land quality scores were calculated and land quality was grad-ed using the model of comprehensive evaluation of multiple factors to make further analysis on characteristics and spatial distribution. The results showed that used agricultural land is dominated by middle and high-grade lands in Jingxi County mainly, representing 68.58% of Jingxi County’s agricultural lands. On the other hand, the quality of paddy land is general y higher than that of dry land, and quality of agricultural land showed higher in middle and eastern Jingxi, and lower in north-western region, which provides references for national land management.
