Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA...Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA)is critical in improving content delivered to end users.Convolutional neural networks(CNNs)used in IQA face two common challenges.One issue is that these methods fail to provide the best representation of the image.The other issue is that the models have a large number of parameters,which easily leads to overfitting.To address these issues,the dense convolution network(DSC-Net),a deep learning model with fewer parameters,is proposed for no-reference image quality assessment(NR-IQA).Moreover,it is obvious that the use of multimodal data for deep learning has improved the performance of applications.As a result,multimodal dense convolution network(MDSC-Net)fuses the texture features extracted using the gray-level co-occurrence matrix(GLCM)method and spatial features extracted using DSC-Net and predicts the image quality.The performance of the proposed framework on the benchmark synthetic datasets LIVE,TID2013,and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.展开更多
为将图像处理技术更好地应用在智能交通中,发挥立体图像质量评价方法的作用,提出一种融合2D和3D卷积神经网络(convolutional neural network,CNN)的立体图像质量评价方法。该模型结合2D-CNN与3D-CNN两个通道;将独眼图输入2D-CNN通道,提...为将图像处理技术更好地应用在智能交通中,发挥立体图像质量评价方法的作用,提出一种融合2D和3D卷积神经网络(convolutional neural network,CNN)的立体图像质量评价方法。该模型结合2D-CNN与3D-CNN两个通道;将独眼图输入2D-CNN通道,提取双目竞争相关特征;将左视图、右视图、和图像和差图像输入3D-CNN通道,通过3D卷积提取双目之间联系的相关特征;应用全连接层,将两个通道提取的特征融合并进行回归分析构建关系模型。在公开的LIVE 3D PhaseⅠ和LIVE 3D PhaseⅡ上的实验结果表明,所提方法与人类的主观感知保持高度一致。展开更多
Traditional image quality assessment methods use the hand-crafted features to predict the image quality score,which cannot perform well in many scenes.Since deep learning promotes the development of many computer visi...Traditional image quality assessment methods use the hand-crafted features to predict the image quality score,which cannot perform well in many scenes.Since deep learning promotes the development of many computer vision tasks,many IQA methods start to utilize the deep convolutional neural networks(CNN)for IQA task.In this paper,a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution,which consists of two tasks:A distortion recognition task and a quality regression task.For the first task,image distortion type is obtained by the fully connected layer.For the second task,the image quality score is predicted during the distortion recognition progress.Experimental results on three famous IQA datasets show that the proposed method has better performance than the previous traditional algorithms for quality prediction and distortion recognition.展开更多
To evaluate the quality of blurred images effectively,this study proposes a no-reference blur assessment method based on gradient distortion measurement and salient region maps.First,a Gaussian low-pass filter is used...To evaluate the quality of blurred images effectively,this study proposes a no-reference blur assessment method based on gradient distortion measurement and salient region maps.First,a Gaussian low-pass filter is used to construct a reference image by blurring a given image.Gradient similarity is included to obtain the gradient distortion measurement map,which can finely reflect the smallest possible changes in textures and details.Second,a saliency model is utilized to calculate image saliency.Specifically,an adaptive method is used to calculate the specific salient threshold of the blurred image,and the blurred image is binarized to yield the salient region map.Block-wise visual saliency serves as the weight to obtain the final image quality.Experimental results based on the image and video engineering database,categorial image quality database,and camera image database demonstrate that the proposed method correlates well with human judgment.Its computational complexity is also relatively low.展开更多
文摘Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA)is critical in improving content delivered to end users.Convolutional neural networks(CNNs)used in IQA face two common challenges.One issue is that these methods fail to provide the best representation of the image.The other issue is that the models have a large number of parameters,which easily leads to overfitting.To address these issues,the dense convolution network(DSC-Net),a deep learning model with fewer parameters,is proposed for no-reference image quality assessment(NR-IQA).Moreover,it is obvious that the use of multimodal data for deep learning has improved the performance of applications.As a result,multimodal dense convolution network(MDSC-Net)fuses the texture features extracted using the gray-level co-occurrence matrix(GLCM)method and spatial features extracted using DSC-Net and predicts the image quality.The performance of the proposed framework on the benchmark synthetic datasets LIVE,TID2013,and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.
文摘为将图像处理技术更好地应用在智能交通中,发挥立体图像质量评价方法的作用,提出一种融合2D和3D卷积神经网络(convolutional neural network,CNN)的立体图像质量评价方法。该模型结合2D-CNN与3D-CNN两个通道;将独眼图输入2D-CNN通道,提取双目竞争相关特征;将左视图、右视图、和图像和差图像输入3D-CNN通道,通过3D卷积提取双目之间联系的相关特征;应用全连接层,将两个通道提取的特征融合并进行回归分析构建关系模型。在公开的LIVE 3D PhaseⅠ和LIVE 3D PhaseⅡ上的实验结果表明,所提方法与人类的主观感知保持高度一致。
文摘Traditional image quality assessment methods use the hand-crafted features to predict the image quality score,which cannot perform well in many scenes.Since deep learning promotes the development of many computer vision tasks,many IQA methods start to utilize the deep convolutional neural networks(CNN)for IQA task.In this paper,a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution,which consists of two tasks:A distortion recognition task and a quality regression task.For the first task,image distortion type is obtained by the fully connected layer.For the second task,the image quality score is predicted during the distortion recognition progress.Experimental results on three famous IQA datasets show that the proposed method has better performance than the previous traditional algorithms for quality prediction and distortion recognition.
基金The National Natural Science Foundation of China(No.61762004,61762005)the National Key Research and Development Program(No.2018YFB1702700)+1 种基金the Science and Technology Project Founded by the Education Department of Jiangxi Province,China(No.GJJ200702,GJJ200746)the Open Fund Project of Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology(No.JETRCNGDSS201901,JELRGBDT202001,JELRGBDT202003).
文摘To evaluate the quality of blurred images effectively,this study proposes a no-reference blur assessment method based on gradient distortion measurement and salient region maps.First,a Gaussian low-pass filter is used to construct a reference image by blurring a given image.Gradient similarity is included to obtain the gradient distortion measurement map,which can finely reflect the smallest possible changes in textures and details.Second,a saliency model is utilized to calculate image saliency.Specifically,an adaptive method is used to calculate the specific salient threshold of the blurred image,and the blurred image is binarized to yield the salient region map.Block-wise visual saliency serves as the weight to obtain the final image quality.Experimental results based on the image and video engineering database,categorial image quality database,and camera image database demonstrate that the proposed method correlates well with human judgment.Its computational complexity is also relatively low.
