Abstract
Visual image quality assessment (IQA) plays a key role in every multimedia application, as end user to it is a human-being. Real time applications demand no reference (NR) IQA, due to unavailability of the reference image. Today, most of the perceived/visual NR-IQA algorithms developed are for distortions like blur, ringing, and blocking artifacts. Very few are available for color distortions. Visible color distortions, such as false color, and zipper are produced in the demosaiced image due to incorrect interpolation of missing color values. In this paper, state of the art zipper and false color artifact quantification algorithms, general purpose NR-IQA algorithms are evaluated for visual quality assessment of demosaiced images. Separate NR- IQA algorithms are proposed for zipper and false color artifact quantification these scores are then combined to obtain final quality score for demosaiced image. Zipper algorithm quantifies zipper artifact by searching for zipper pixels in an image. While, false color algorithm finds correlation between local high frequency region’s color planes to quantify false color.
Highlights
Image quality assessment plays key role in image processing pipeline for testing and validation
The quality of the test image is expressed as the distance between multivariate Gaussian fits of the Natural Scene Statistic (NSS) features extracted from test image and the multivariate Gaussian model of the quality aware features extracted from the corpus of natural images
6) Average out the correlation values obtained for high frequency regions of G&R and G&B planes to obtain proposed false color quantification score
Summary
Image quality assessment plays key role in image processing pipeline for testing and validation. Quality assessment of demosaiced image and testing of demosaicing algorithm is performed in the RGB color space or International Commission on Illumination’s L* a* b* (CIELAB) color space with FR objective quality metrics such as color peak signal to noise ratio (CPSNR), color mean square error (CMSE), CIELAB color difference deltaE (CIELAB ΔE), or spatial extension of the CIELAB color difference deltaE (SCIELAB ΔE) [3] As these metrics are FR, so they have limitation for real time application. Separate novel visual NR-IQA algorithms are proposed for zipper and false color artifacts These scores are combined to obtain final visual quality score for demosaiced image.
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