Research On Image Quality Evaluation Method Based On Visual Perception

Under the ever-changing development of information technology,the field of computer vision has developed rapidly under the support of new theories,opening up new research methods and ideas.As a necessary source of information in human production and life,images provide a wealth of knowledge.Image processing technology plays an important role in the 21 st century of electronic network and is also a catalyst for innovation and progress in human society.Image quality is one of the most significant aspects in the field of image research.Image quality provides an important basis for quantifying image and video performance for many computer vision-related research work.Image is a direct and efficient way for humans to obtain information,the subjective quality evaluation score which provided by many professional observers has accurate statistical significance.However,this method not only requires a lot of manpower and material resources,but also face the problem that it cannot work in the real-time processing system.Therefore,the objective quality assessment method which by constructing a computational model to simulate the human visual system has very important application value in practical applications.With the deepening of deep learning technology,deep networks such as convolutional neural networks have produced notable results in the image domain of the classification and recognition tasks.The representative features can be extracted automatically by VGG-NET which belongs to convolutional neural network.It is very robust in the field of compute vision.In this paper,a full reference image quality evaluation method based on human visual system is proposed.The most relevant characteristics of human visual system are considered.The convolutional neural network is used to calculate the feature map of the image,with the characteristics of the image and the contour map are combined to effectively evaluation quality of images.In this paper,VGG-NET is used to extract image features.Considering that the image library for quality evaluation contains a small amount of data,firstly,the natural image network training is performed in the big data set Image-Net,and then the trained network is migrated to the image quality evaluation.The calculation of the image feature map is performed in the database.Related studies have shown that humans tend to pay more attention to the region of interest in the image when observing the image.The saliency detection of the image conforms to the visual characteristics of the human eye,emphasizing the key information in the image while ignoring many redundant information that is not eye-catching.The brightness information of the image is very important in the human visual system.The color information can reflect the area that the human eyes pay more attention to.Therefore,this paper aims to improve the method of image saliency detection by combine the luminance feature with the detection algorithm of color feature.Insufficient brightness and distance information,this paper proposes a color brightness saliency detection method based on center distance.By calculating the brightness and color in the image,the saliency map of the reference image and the distorted image is obtained,based on the saliency of the image color and brightness.The detection method can effectively assist in the quantification of image quality.At the same time,because the texture contour of the image will have a certain loss according to the degree of distortion of the image,this paper uses Mean-Shift and Gaussian pyramid to detect the contour of the image and generate the edge contour map of the image as an important reference information for image quality evaluation.In this paper,the original image and the feature map generated by these three methods are organically combined,and the feature map output by the neural network is used as the weight of the quality measurement.A full reference image quality detection algorithm conforming to the characteristics of the human visual system is proposed.The experimental results show that the proposed algorithm and the human visual system have higher consistency.Compared with classical ful-reference image quality evaluation methods,the proposed algorithm performs better on the evaluation index such as SROCC,PLCC and RMSE.The algorithms in this paper on TID,CSIQ and other datasets also show high consistency with the human visual system.