Blind Image Quality Assessment Based On Visual Characteristics And Statistical Analysis

With the development of multimedia technology and the Internet,images have gradually become a simple and efficient way to carry information,playing a vital role in people's daily lives.However,the image may have quality degradation effects at various stages of acquisition,processing,transmission,and storage,which not only affects the user's visual experience but also is not conducive to subsequent application.Therefore,it is important to propose effective image quality assessment(IQA)methods through analysis and modeling.The image signal is received by the human eye and transmitted to the visual cortex.The human visual system(HVS)determines the form in which the image is interpreted and understood,resulting in subjective judgments such as beauty and badness.Image statistical analysis explores ubiquitous and effective statistical laws from a statistical perspective.This thesis combines characteristics of the HVS and statistical analysis of images to explore and establish blind image quality assessment(BIQA)methods that are highly compatible with the subjective perception of human eyes.The research work of this thesis mainly includes the following two aspects:A no-reference quality evaluation method based on global and local visual perception is proposed for tone-mapped images.Firstly,the characteristics of tonemapped images are analyzed,and the HVS is simulated to perceive image signals from global and local levels.Global perception is combined with statistical analysis.Specifically,color moments are used to describe color information,light and dark distribution features are designed to measure the overall exposure degree and information entropy is used to calculate the global information.In terms of local perception,images are divided into many blocks.Then,the contrast difference and local entropy of the block are calculated,and the image signal is decomposed and calculated in the discrete wavelet transformation(DWT)domain in combination with the multichannel decomposition mechanism.Combining the characteristics of the global and local levels,the machine learning method is used to regress the features to obtain the evaluation model.The experimental results on the ESPL-LIVE HDR database show that the proposed algorithm has a 3% higher Pearson Linear Correlation Coefficient(PLCC)and Spearman Rank-order Correlation Coefficient(SRCC)than the current state-of-the-art algorithm,and has a high consistency with the subjective score.A no-reference quality evaluation method based on structure,texture and color information is proposed for authentically distorted images.Considering the complexity of authentically distorted images,the HVS sensitive multi-attributes are used for visual perception.The perception of each attribute is not the result of a single nerve cell response.Therefore,each attribute is statistically represented by various features.Among them,the HVS multi-attribute perception characteristics are mainly characterized by three aspects: structure,texture,and color information.In terms of structural information,gradient histogram and exposure are utilized.In the aspect of texture information,DWT decomposition is performed and its logarithmic energy is computed.Concerning color information,it is divided into chroma perception and saturation perception,in which chroma information is obtained from global and local levels.Finally,combined with the above three kinds of information,a quality-sensitive prediction model is obtained by using support vector regression.The experimental results show that the proposed algorithm has a PLCC of 0.8709 and an SRCC of 0.8059 on the CID2013 database,which is highly consistent with the subjective perception score and is superior to the existing evaluation algorithm.