Research And Implementation Of No- Reference Image Quality Assessment Based On Multiple Scales And Multiple Responses

With the rapid development of information technology and people's pursuit of life quality,image plays an increasingly important role in people's daily life.However,due to various reasons,an image will be distorted to a certain extent,the quality of the image will be reduced to a certain extent,which has caused some trouble to people's daily use.Therefore,the domain of image quality assessment has become a hot topic in the field of computer vision.Image quality assessment can be divided into subjective assessment and objective assessment.However,subjective methods based on human observation are timeconsuming and laborious,and their practicability is poor.Therefore,the current mainstream image quality assessment methods are objective evaluation methods,mainly through training algorithm model to evaluate image quality.In this paper,the focus of the research is on the no-reference image quality assessment in objective methods.The main research work is as follows:(1)A no-reference image quality assessment method based on Contourlet multiple scales transform is proposed.The multiple scales theory is applied to the field of image quality assessment,and the non-subsampled Contourlet transform is used to decompose the image at multiple scales.The obtained multiple scales images are extracted from the image mean gradient,the visual saliency feature of the phase spectrum and the information entropy.The support vector machine is used for the training test.Through experimental analysis,it is found that the algorithm has certain advantages compared with other popular algorithms.(2)A no-reference image quality assessment method based on multiple scales and multiple responses is proposed.First of all,the limitations of the traditional Laplacian Gaussian operator are analyzed,and then the Laplacian Gaussian operator is optimized by adding bilateral filtering.The non-subsampled Contourlet multiple scales transform is performed on the test image and then the optimized Laplacian Gaussian operator object is used for multiple responses operation,so that the image under multiple scales and multiple responses can be obtained.Then the generalized Gaussian model parameters,frequency variation coefficients,directional features and gradient histogram distances under multiple scales and multiple responses are extracted as features to describe the image quality.Experiments prove that the trained model can perform well and can objectively reflect the image quality.(3)The design of no-reference image quality assessment system based on multiple scales and multiple responses is realized.The proposed algorithm is implemented systematically,and an easy-to-use,no-reference image quality evaluation system is designed,which can evaluate the image quality.