Research On Image Quality Assessment Based On Visual Perception And Statistics

Nowadays,as the internet and communication technology develop rapidly,digital images have become the important means of information transmission in daily life.According to the statistics,the total amount of digital images produced in the world attains tens of billions from the year of 2011,such number is still increasing year by year.However,the digital images are vulnerable to different kinds of distortions during acquisition,storage,compression and transmission.Therefore,the image quality is often affected at different levels.How to accurately evaluate the image quality becomes an important research hotspot in current days and in the future days.Generally,most of the images are consumed by humans.Then the most reliable way for evaluating the image quality is the subjective quality assessment,which refers to asking viewers to rate the image quality according to their perception of the image quality.However,subjective quality assessment is often hard to carry out because of the large amount of images.In addition,subjective quality assessment can't be used in the real-time image processing applications.Toward this end,researchers attempt to develop objective quality assessment methods for image quality evaluation by means of objective algorithms.According to the accessibility of the original image,existing objective image quality assessment methods can be classified into three categories,which are full-reference(FR),reduced-reference(RR)and no-reference(NR)methods respectively.Although a lot of objective methods belonging to these three types have been proposed,the research on objective quality assessment is still immature,which can be manifested as follows: First,due to lack of understanding about the human visual perception mechanism,existing objective methods can't simulate the subjective quality assessment accurately;Second,in the design of NR methods,most methods still resort to subjective scores for training the quality model;Third,to assess the real distorted images,existing objective methods still perform poorly.Focusing on the above issues,this dissertation carries out research on RR and NR methods in objective IQA from the perspective of the human visual perception mechanism and statistical modeling.The content of the dissertation can be divided into four sections detailed as follows:First,due to lack of understanding about the human visual perception mechanism,most existing methods mainly assess the image quality by measuring the distortion degree of the signal itself,this kind of methods can't evaluate the image quality accurately without considering the human visual perception mechanism.Based on the exploration of the human visual perception mechanism,a RR image quality evaluation method based on the free-energy principle and sparse representation is proposed.The free-energy principle in recent studies of brain theory and neuroscience models the perception and understanding of the outside scene as an active inference process,in which the brain tries to account for the visual scene with an internal generative model.Specifically,with the internal generative model,the brain yields corresponding predictions for its encountered visual scenes.Then the discrepancy between the visual input and its brain prediction should be closely related to the quality of perceptions.On the other hand,sparse representation has been evidenced to resemble the strategy of the primary visual cortex in the brain for representing natural images.With the strong neurobiological support for sparse representation,in this dissertation,we approximate the internal generative model with sparse representation and propose an image quality metric accordingly,which is named FSI(Free-energy principle and Sparse representation-based Index for image quality assessment).In FSI,the reference and distorted images are respectively predicted by sparse representation at first.Then the difference between the entropies of the prediction discrepancies is defined to measure the image quality.Experimental results confirm the superiority of the proposed method over the same kind of methods.The time complexity is also lower in the meantime.Although our method belongs to RR methods,it only needs a single number from the reference image,which maximumly reduces the needed data amount from the original image for quality evaluation.Second,most of the existing NR methods belong to the opinion-aware methods,which require a large number of image samples with the associated subjective scores for training the quality model.By comparison,opinion-unaware methods are very limited and existing opinion-unaware methods still can't be compared with the opinion-aware methods.Therefoer,a highly effective opinion-unaware NR method is proposed,in which we attempt to quantify the image quality degradations through measuring the structure,naturalness and the perception quality variations of the distorted image from the pristine images.In our method,the structure is characterized by modeling the image phase congruency(PC)and image gradients distributions.The naturalness degree is measured through the parameters that depict the distributions of the locally mean subtracted and contrast normalized(MSCN)coefficients and the products of pairs of the adjacent MSCN coefficients.The perception quality is characterized by the prediction discrepancy between the image and its brain prediction based on the free-energy principle.After feature extraction,we learn a pristine multivariate Gaussian(MVG)model with the extracted features from a set of pristine images,which is used for quality definition.The quality of a new image is defined as its MVG model variation from the pristine MVG model.Experimental results demonstrate the proposed method outperforms state-of-the-art opinion-unaware methods and delivers comparative performance with mainstream opinion-aware methods.Third,during image acquisition,the images are possibly distorted by different kinds of distortions,such as noise,blur,contrast change because of the improper camera settings or the casual photographing manner.Among the distortions,out-of-focus blur occupies a large proportion.However,the specific quality assessment to this kind of distorted images is still limited.Therefore,in this dissertation,we focus our attention on the quality assessment for the out-of-focus blurred images and propose a specific blind quality assessment model to evaluate the quality of the real out-of-focus blurred images.At first,we establish a specific out-of-focus blurred image database through subjective experiments.Then we propose a specific quality model to evaluate the out-of-focus blurred image quality.In our model,we employ the gradients and PC features to extract the structure information of the image for measuring the blurriness degree.Considering the arbitrary distribution of the out-of-focus blurr over the image,we then perform saliency detection on the image and utilize the saliency map to weight the local blurriness map accordingly.The quality of the image is finally estimated by pooling the weighted blurriness map.Experimental results demonstrate the proposed method delivers high consistency with subjective evaluation results.Fourth,although existing quality assessment methods earn high prediction performance on the existing databases,the distortions in those databases are artificially generated,which is quite different from the distortions in real camera images.The distortions in real images are much complex,which raises great challenge for precisely evaluating the image quality.For accurately evaluating the quality of real camera images,a blind quality assessment method based on neural network for real camera images is proposed in this dissertation.In our proposed method,we extract two types of quality-aware statistical features to characterize the image quality degradations.The first type of statistical features are extracted from the locally mean subtracted and contrast normalized(MSCN)coefficients,which describe the low-level characteristics in the early human vision.The second type of features are extracted from the distribution of the prediction discrepancy between the image and its brain prediction based on the free-energy principle,which is to characterize the high-level characteristics of the human visual perception.After feature extraction,we design a neural network of four layers to evaluate the image quality.Experimental results demonstrate the proposed method achieve higher prediction accuracy than the existing blind quality methods.