37,077 research outputs found

    A No Reference Objective Color Image Sharpness Metric

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    International audienceIn this work, we propose a no reference color image quality assessment metric. The proposed metric makes use of a wavelet-based multiscale structure tensor [1] as an extension of the single-scale structure tensor proposed by Di Zenzo [15]. The multiscale structure tensor allows for accumulating multiscale gradient information of local regions of the color image. Thus, averaging properties are maintained while preserving edge structure. This structure tensor is capable of identifying edges in spite of the presence of noise. Once edges are identified, we define a sharpness metric based on the eigenvalues of the multiscale structure tensor. Particularly, we show that the difference of the eigenvalues of the multiscale structure tensor can be used to measure the sharpness of color edges. Based on this fact we formulate our no reference sharpness metric for color images. Experiments performed on LIVE database indicate that the objective scores obtained by the proposed metric agree well with the subjective assessment score

    Diffusion Tensor Imaging: on the assessment of data quality - a preliminary bootstrap analysis

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    In the field of nuclear magnetic resonance imaging, diffusion tensor imaging (DTI) has proven an important method for the characterisation of ultrastructural tissue properties. Yet various technical and biological sources of signal uncertainty may prolong into variables derived from diffusion weighted images and thus compromise data validity and reliability. To gain an objective quality rating of real raw data we aimed at implementing the previously described bootstrap methodology (Efron, 1979) and investigating its sensitivity to a selection of extraneous influencing factors. We applied the bootstrap method on real DTI data volumes of six volunteers which were varied by different acquisition conditions, smoothing and artificial noising. In addition a clinical sample group of 46 Multiple Sclerosis patients and 24 healthy controls were investigated. The response variables (RV) extracted from the histogram of the confidence intervals of fractional anisotropy were mean width, peak position and height. The addition of noising showed a significant effect when exceeding about 130% of the original background noise. The application of an edge-preserving smoothing algorithm resulted in an inverse alteration of the RV. Subject motion was also clearly depicted whereas its prevention by use of a vacuum device only resulted in a marginal improvement. We also observed a marked gender-specific effect in a sample of 24 healthy control subjects the causes of which remained unclear. In contrary to this the mere effect of a different signal intensity distribution due to illness (MS) did not alter the response variables

    Image Quality Assessment Based on Detail Differences

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    This paper presents a novel Full Reference method for image quality assessment based on two indices measuring respectively detail loss and spurious detail addition. These indices define a two dimensional (2D) state in a Virtual Cognitive State (VCS) space. The quality estimation is obtained as a 2D function of the VCS, empirically determined via polynomial fitting of DMOS values of training images. The method provides at the same time highly accurate DMOS estimates, and a quantitative account of the causes of quality degradation
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