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Visual Information Quality Perceptual Model And Assessment Metrics

Posted on:2014-06-06Degree:DoctorType:Dissertation
Country:ChinaCandidate:L H HeFull Text:PDF
GTID:1268330431462455Subject:Pattern Recognition and Intelligent Systems
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Visual information digitization has spread to every corner of the world, and peoplewill continue to pursue high definition and fidelity of visual information. However, theprocesses of acquisition, compression, processing, transmission and restorationintroduce different distortions to the visual information. This gives a great obstacle tothe process of visual information processing, analysis and interpretation, preventinghuman beings from understanding the objective world. Therefore, it is necessary todesign reasonable and reliable methods to measure perceived quality of visualinformation. These methods have guiding significance for optimizing, improving andoptimizing the visual information processing system. Thus the system can provide thebest visual quality with minimum cost.In this dissertation, a systematic study about fundamental issues of the qualityperception model and assessment metrics for visual information is carried out. Basedupon the fundamental characteristics of human visual system, this study explores theperceptual characteristics, analyzes the statistical regularities of natural scenes, modelsthe hierarchical quality semantic and finally constructs several objective qualityassessment methods. These methods measure the fidelity and intelligibility of visualinformation and provide a reasonable basis for the design and optimization of visualinformation processing system. The author’s major contributions are outlined as follows(1) Considering that the Human Visual System (HVS) has different perceptualcharacteristics for different morphological components, a novel image quality metric isproposed by incorporating Morphological Component Analysis (MCA) and HVS,which is capable of assessing the image with different kinds of distortion. Firstly,reference and distorted images are decomposed into linearly combined texture andcartoon components by MCA respectively. Then these components are turned intoperceptual features by Just Noticeable Difference (JND) which integrates maskingfeatures, luminance adaptation, and Contrast Sensitive Function (CSF). Finally, thediscrimination between the feature of the reference image and that of the distortedimage is quantified using a pooling strategy before the final image quality is obtained.Experimental results demonstrate that the performance of the proposed prevails oversome existing methods.(2) Image quality assessment based on S-CIELAB model. Most natural sceneimages captured by digital devices are color images, which bear huge information. Theloss of the color information caused by the distortion has great impact on the perceptual image quality. However, most existing IQA metrics are only designed for gray images.Hence, the S-CIELAB color model, which has an excellent performance for mimickingthe perceptual processing of human color vision, is incorporated with the geometricaldistortion measurement to assess the image quality. First, the reference and distortedimages are transformed into the S-CIELAB color perceptual space, and the transformedimages are evaluated by an existing metric in three color perceptual channels. Thefidelity factors of three channels are weighted to obtain the image quality. Experimentalresults show that the proposed methods are in good consistency with human subjectivequality scores.(3) Color fractal structure model for reduced-reference colorful image qualityassessment. Most of existing methods fail to take color information into consideration,although the color distortion is significant for the increasing color images. To solve theaforementioned problem, this paper proposed a novel IQA method which focuses on thecolor distortion. In particular, we extract color features based on the model of colorfractal structure. Then the color and structure features are mapped into visual qualityusing the support vector regression. Experimental results demonstrate that the proposedmethod is highly consistent with the human perception especially on images with colordistortion.(4) Sparse representation for no-reference image quality assessment. Since theexisting no-reference image quality assessment (IQA) is designed for the image withone or few specific distortions, a universal no-reference image quality assessmentmetric is introduced. It is a simple yet effective algorithm based upon the sparserepresentation of natural scene statistics (NSS) feature and it can predict the quality ofimage with different distortions. This algorithm consists of three key steps: extractingNSS features in the wavelet domain, representing features via sparse coding, andweighting differential mean opinion scores by the sparse coding coefficients to obtainthe final visual quality values. Thorough experiments show that the proposed algorithmis consistent with subjective perception values, and outperforms representative blindimage quality assessment algorithms and some full-reference metrics.(5) Visual-quality-related topics based “completely” no-reference image qualityassessment. Because visually salient areas are critical in subjective quality assessment,visual saliency weighted hierarchical Dirichlet processes (visual-saliency wHDP) isproposed by introducing visual saliency as the prior for observations to dominate theconstruction of latent topics. According to this model, no-reference image quality assessment metric is proposed, and it includes three parts: constructing visualvocabulary by extracting the quality-aware features from a training set, obtainingdistributions of visual-quality-related topics by training visual saliency wHDP, andestimating the quality of a test image by computing the difference between thevisual-quality-related topics found in the test image and those found in the originalimages contained in the training set. Thorough experiments show that the proposedvisual-saliency wHDP robustly produces quality-related topics and obtains promisingperformance.Extensive study has been conducted from full-reference IQA to no-reference IQA,which differs in the dependence of the references and practicability. The overallresearch on visual information perceptual model and quality assessment follows thegradual progress that is from the elementary to the profound. The fruit presented in thisdissertation opens up a new way for visual information assessment, which hasextremely important theoretical significance and practical value.
Keywords/Search Tags:Visual Information, Quality Assessment, Human Visual System, Just Noticeable Difference, Color Vision, Sparse Representation, Natural Scene Statistic, Visual-quality-related Topic
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