Image Quality Assessment Using Spatial Frequency Component

Image quality assessment (IQA) is a crucial technique in perceptual image/video coding, because it is not only a ruler for performance evaluation of coding algorithms but also a metric for ratio-distortion optimization in coding. In this paper, inspired by the fact that distortions of both global and local information influence the perceptual image quality, we propose a novel IQA method that inspects these information in the spatial frequency components of the image. The distortion of the global information mostly existing in low spatial frequency is measured by a rectified mean absolute difference metric, and the distortion of the local information mostly existing in high spatial frequency is measured by SSIM. These two measurements are combined using a newly proposed abruptness weighting that describes the uniformity of the residual image. Experimental results on LIVE database show that the proposed metric outperforms the SSIM and achieves performance competitive with the state-of-the-art metrics. ? 2009 Springer-Verlag Berlin Heidelberg.EI

Hemispheric differences in visual non-verbal processing

SIGLEAvailable from British Library Lending Division - LD:D53971/85 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

The perceived orientation of aliased lines

AbstractThe use of raster display devices for the display of graphics causes problems of aliasing when edges or lines are produced. This can be significant in those psychophysical experiments where the orientational properties of the stimulus are important. We have assessed the perceived orientation of a selection of abased lines by comparing them with the orientation of pairs of dots. It is found that the perceptual orientation is modelled well by a least-squares metric on the pixels that compose the line. Small deviations from this metric were found, and were also found in a control experiment employing anti-aliased lines. They appear to be due to range effects. Averaged across subjects, orientational acuity was only slightly lower for aliased lines

Manpower planning in Jordan

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Correlation-based intrinsic image extraction from a single image

Abstract. Intrinsicimagesrepresenttheunderlyingpropertiesofascene such as illumination (shading) and surface reflectance. Extracting intrinsic images is a challenging, ill-posed problem. Human performance on tasks such as shadow detection and shape-from-shading is improved by adding colour and texture to surfaces. In particular, when a surface is painted with a textured pattern, correlations between local mean luminance and local luminance amplitude promote the interpretation of luminance variations as illumination changes. Based on this finding, we propose a novel feature, local luminance amplitude, to separate illumination and reflectance, and a framework to integrate this cue with hue and texture to extract intrinsic images. The algorithm uses steerable filters to separate images into frequency and orientation components and constructs shading and reflectance images from weighted combinations of these components. Weights are determined by correlations between correspondingvariations inlocal luminance, local amplitude,colour andtexture. The intrinsic images are further refined by ensuring the consistency of local texture elements. We test this method on surfaces photographed under different lighting conditions. The effectiveness of the algorithm is demonstrated bythecorrelation between ourintrinsic images andground truth shading and reflectance data. Luminance amplitude was found to be a useful cue. Results are also presented for natural images.

Detection of distortions in images of natural scenes in mild traumatic brain injury patients

Psychological Health and Traumatic Brain Injury Research Progra