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Research On Subjectively-consistent Objective Image Quality Assessment

Posted on:2015-03-06Degree:DoctorType:Dissertation
Country:ChinaCandidate:A Z HuFull Text:PDF
GTID:1268330428999926Subject:Signal and Information Processing
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Perceptual image quality assessment (IQA), which aims at the evaluation of quality degration due to image distortions, has become a fundamental and challenging task in the field of image processing. A reliable IQA method can be used to dynamically monitor image/video quality, choose the parameters in a coding system, and benchmark image acquisition and processing systems. The most accurate IQA method is the subjective viewing test. However, subjective evaluation s is as cumbersome in practice as it is expensive, complex, and time-consuming. On the other hand, the traditional mathematical statistic-based error measurements, such as PSNR and MSE, do not match well with subjective perception. Therefore, in order to further promote the development of image processing technology, it is need to develop IQA metrics based on human vision and perception.Based on the engineering assessment framework that received widespread attention for lower complexity and better performance in recent years and human visual system (HVS) peculiarities that used in the conventional vision bionics methods, this thesis investigates the design of efficient IQA algorithms by employing the two stages of the engineering framework:local distortion measure and distortion feature pooling, and proposed a new engineering algorithm by using the idea of content information extraction. In addition, this thesis also studies the perceptual quality assessment of synthetic aperture radar (SAR) image compression. The main work is detailed as follows:(1) The HVS peculiarities from the physiology and psychophysics researches have been summarized as well as the corresponding signal and information processing technologies. It provides the theoretical basis for designing effective perceptual IQA algorithms.(2) A local distortion measure algorithm has been designed by incorporating the interaction of spatial and spectral sensitivities of HVS. The general idea of the proposal is using different local quality measurements according to specific image region. First, a region partition algorithm is utilized to segment the image into complex and smooth areas. Then, a simple SSIM or a wavelet-based SSIM is chosen for each local region according to the result of region partition, to obtain local image qualities. Finally, the local image qualities are merged into a single quality score. Experiment results show that the proposal outperforms the improved SSIM algorithms only considering the spatial or spectral sensitivities of HVS.(3) The distortion pooling model has been studied for spatial and spectral domain, respectively. For spatial domain, first, a structural entropy weighting algorithm is developed by considering the visual phenomenon of lateral inhibition. Secondly, a structural entropy-based variable-scale spatial pooling model is proposed by mimicking the space-variant sampling nature of HVS. For spectral domain, an artificial neural network-based multi-channel evaluation pooling strategy is realized by introducing the machine learning techniques. Experiment results show that, in the case of the same local distortion assessment scheme, the proposed pooling models outperforms traditional models based on visual attention and CSF weighting.(4) A general IQA algorithm based on image content information extraction is proposed inspired by the justification that image distortions will lead to the change and loss of image content. First, the scale invariant feature transform (SIFT) is employed to extract the local content keypoints. Secondly, by comparing the feature matching results and feature similarity between the reference and the test images, the global and detailed content distortions are evaluated. Then, the content distortion measures and the conventional structual distortion measure are integrated into the final IQA result though adaptive weighting. Experiment results show that the proposal outperforms the top engineering method, VIF, on multiple publicly available databases.(5) The study of visual quality assessment of synthetic aperture radar (SAR) image compression in accord with subjective perception has been carried out. First, a psychometric study that contained four SAR image compression techniques and a total of300test images was carried out to obtain subjective evaluation results. Then, for SAR image compression, a special IQA algorithm based on image content classification and support vector regression is proposed by taking into account the characteristics of the SAR image and HVS. Experiment results show that the visual quality of the compressed images may be slightly better than the original images due to the presence of speckle. And the proposed objective method has a perfect and robust performance for predicting the perceptual quality of SAR image compression.To sum up, according to the perceptual properties of human vision, this thesis has presented several IQA algorithms for natural image and SAR image compression based on multi-channel decomposition, visual attention, image understanding, machine learning, and subjective assessment experiment, and the provided experiments have demonstrated their reliability and effectiveness.
Keywords/Search Tags:Human visual system (HVS), image quality assessment (IQA), multi-channel model, visual attention, machine learning, scale invariantfeature transform (SIFT), subjective assessment experiment, syntheticaperture radar (SAR) image compression
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