Visual instrument image-quality assessment with rotationally symmetric difference of Gaussians

The prediction of an image quality metric (IQM) should be consistent with subjective human evaluation. As the human visual system (HVS) is critical to visual perception, modeling of the HVS is regarded as the most suitable way to achieve perceptual quality predictions. Sparse coding that is equivalent to independent component analysis (ICA) can provide a very good description of the receptive fields of simple cells in the primary visual cortex, which is the most important part of the HVS. With this inspiration, a quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA) on the basis of transformation of images into sparse representations in the primary visual cortex. The proposed method is based on the sparse features that are acquired by a feature detector, which is trained on samples of natural images by an ICA algorithm. In addition, two strategies are designed to simulate the properties of the visual perception: 1) visual attention and 2) visual threshold. The computation of SFF has two stages: training and fidelity computation, in addition, the fidelity computation consists of two components: feature similarity and luminance correlation. The feature similarity measures the structure differences between the two images, whereas the luminance correlation evaluates brightness distortions. SFF also reflects the chromatic properties of the HVS, and it is very effective for color IQA. The experimental results on five image databases show that SFF has a better performance in matching subjective ratings compared with the leading IQMs.

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Sparse Feature Fidelity for Perceptual Image Quality Assessment

Chang

Yang

Gan

et al. 2013

IEEE Trans. on Image Process.

149

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Mission-driven evaluation of imaging system quality

2001

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Image-quality criteria are usually intended to achieve the best possible image at a given sampling rate, which is ill-suited to applications where the detection of well-defined geometric and radiometric properties of scenes or objects are paramount. The quality criterion developed here for designing observation systems is based on properties of the objects to be viewed. It is thus an object-oriented imaging quality criterion rather than an image-oriented one. We also propose to go beyond optimization and calibrate a numerical scale that can be used to rate the quality of the service delivered by any observation system.

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Visual instrument image-quality assessment with rotationally symmetric difference of Gaussians

Cited by 2 publications

References 7 publications

Sparse Feature Fidelity for Perceptual Image Quality Assessment

Sparse Feature Fidelity for Perceptual Image Quality Assessment

Mission-driven evaluation of imaging system quality

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