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Title: No-Reference Quality Assessment of digital images
Keywords: image quality assessment, kurtosis, no-reference image quality assessment, pixel activity, structural activity, visual quality
Issue Date: 16-Aug-2010
Citation: ZHANG JING (2010-08-16). No-Reference Quality Assessment of digital images. ScholarBank@NUS Repository.
Abstract: Objective image quality measures have been developed to quantitatively predict perceived image quality. They are of fundamental importance in numerous applications, such as to benchmark and optimize different image processing systems and algorithms, to monitor and adjust image quality, and to develop perceptual image compression and restoration technologies, etc. As an important approach for objective image quality assessment, no-reference image quality assessment seeks to predict perceived visual quality solely from a distorted image and does not require any knowledge of a reference (distortion-free) image. No-reference image quality measures are desirable in applications where a reference image is expensive to obtain or simply not available. The intrinsic complexity and limited knowledge of the human visual perception pose major difficulties in the development of no-reference image quality measures. The field of no-reference image quality assessment remains largely unexplored and is still far from being a mature research area. Despite its substantial challenges, the development of no-reference image quality measures is a rapidly evolving research direction and allows much room for creative thinking. The number of new no-reference image quality measures being proposed is growing rapidly in recent years. This thesis focuses on the development of no-reference image quality measures. One contribution of this thesis is the kurtosis-based no-reference quality measures developed for JPEG2000 compressed images. The proposed no-reference image quality measures are based on either 1-D or 2-D kurtosis in the discrete cosine transform domain of general image blocks. They are simple, they do not need to extract edges/features from an image, and they are parameter free. Comprehensive testing demonstrates their good consistency with subjective quality scores as well as satisfactory performance in comparison with both representative full-reference image quality measures and state-of-the-art no-reference image quality measures. The second contribution of this thesis is a pixel activity-based no-reference quality measure developed for JPEG2000 compressed images. Based on the basic activity of general pixels, the proposed no-reference quality measure overcomes the limitations imposed by structure/feature extraction of distorted images. The structural content-weighted pooling approach in the proposed image quality measure does not require any parameters and avoids additional procedures and training data for parameter determination. The proposed image quality measure exhibits satisfactory performance with reasonable computation load and easy implementation. It proves a no-reference quality measure of choice for JPEG2000 compressed images. The third contribution of this thesis is the development of a structural activity-based framework for no-reference image quality assessment. Under the assumption that human visual perception is highly sensitive to the structural information in a scene, such a framework predicts image quality through quantifying the structural activities of different visual significance. As a specific example, a model named structural activity measure is developed. The model is validated with a variety of distortions including white noise, Gaussian blur, and JPEG and JPEG2000 compression. The effectiveness of the model is demonstrated through the comparison with subjective quality scores as well as representative full-reference image quality measures. The structural activity-based framework proves effective for no-reference image quality assessment. The work presented in this thesis is not limited to the development of effective techniques for no-reference image quality assessment. It may also contribute to a better understanding of the working mechanisms underlying human visual perception.
Appears in Collections:Ph.D Theses (Open)

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