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Research on Novel Methodes of Reduced Reference and No Reference Image Quality Assessment

Author: SangQingBing
Tutor: WuXiaoJun
School: Jiangnan University
Course: Light Industry Information Technology and Engineering
Keywords: Image Qulity Assessment No Reference Reduced Reference WaveletTransform Singular Value Decomposition Discrete Cosine Transform Gray LevelCo-occurrence Matrix Phase Congruency Support Vector Regression Singular Value Curve
CLC: TP391.41
Type: PhD thesis
Year: 2013
Downloads: 164
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Abstract


In recent years, the increasing number of demanding consumer image applications hasboosted interest in objective image quality assessment (IQA) algorithms. Objective imagequality assessment aims to automatically measure the quality degradation perceived by thehuman eyes. It is of fundamental importance to address a wide variety of problems in imageand video processing. Based on the availability of the information about the reference image,IQA models can be classified into full-reference (FR), reduced-reference (RR) and noreference (NR) IQA methods. This dissertation focuses on RR-IQA AND NR-IQA, the majorcontents are as follows in general:First, we propose a novel metric for RRIQA based on wavelet transform. We dothe wavelet decomposition of2scales to images, and extract the low frequency waveletcoefficients of the second scale as the image feature vector. In order to measure thesimilarity of vectors, we see the feature vector as a point in n-dimensional Euclidean space,and calculate the distance between referenc image feature vector and distortion image featurevector in n-dimensional Euclidean space. The distance is regarded as the metric of imagequality.Second, we propose a no reference blur image quality assessment method based on graylevel co-occurrence matrix extraction phase congruency image feature and supportvector regression (SVR). The method is composed of three steps. First, we use Log Gaborwavelet to generate phase congruency map of the image. Then we calculate the PhaseCongruency map’s features which are entropy, energy, contrast, correlation and homogeneityby gray level co-occurrence matrix. Finally, we predict no-reference blur image quality scoreby using SVR model training and learning.Third, we propose a no reference blur image quality assessment method based ondiscrete cosine transform. First of all, we do discrete cosine transform to images and extractdiscrete cosine transform coefficients as feature vector, and then used the generalizedregression neural network model to train feature vector to predict image quality. In the threepublic databases, the experimental results show that this method has a good correlation withthe subjective quality score.Fourth, we propose a new blind blur and noise index for still images using Gaussian blurand Singular Value Decomposition (SVD). The algorithm is composed of three steps. Firstly,a re-blurred image is produced by using Gaussian blur to the test image. Then the singularvalue decomposition is performed to the test image and re-blurred image. Finally, a blur andnoise index is constructed by using the change of singular values. Experimental resultsobtained on four simulated databases show that the proposed algorithm has high correlationwith human judgments when assessing blur distortion of images.Finally, we propose two universal blind image quality assessment methods. The first is auniversal blind image quality assessment method based on the change of singular value. Thechange of singular value can reflect the distortion of an image. The second is a universal blindimage quality assessment method using a reciprocal singular value curve. The reciprocalsingular value curves of natural images resemble inverse power functions. The bending degree of the reciprocal singular value curve is varies with distortion type and severity. Weconstructed two new general blind IQA indices utilizing the area and curvature of imagereciprocal singular value curves. These two methods do not require prior knowledge of anyimage or distortion, and hence do not require any process of training, hence are "completelyblind" IQA models.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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