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SC-VQM – A Visual Quality Metric for Synthetic Contents

Christopher Haccius and Thorsten Herfet
Telecommunications Lab, Saarland University, Saarbrücken, Germany
Abstract—Metrics approximating the perceived visual quality of image or video content play an important role in the development and usage of manifold processing and compression algorithms. Several visual quality metrics have been proposed in the past and produce good results. However, visual content is more and more influenced by synthetic content generation. Fully synthetic scenes or augmented reality contents present a new challenge to visual quality metrics. We present a Visual Quality Metric for Synthetic Contents (SC-VQM), which considers visual errors common to synthetic sources. We have tested our metric on an image quality database. Comparisons of the correlation between predicted Mean Opinion Score and subjective Mean Opinion Score show that our proposed Visual Quality Metric discriminates perceived visual quality significantly better than known standard quality metrics (PSNR, SSIM, HDR-VDP 2).
Index Terms—subjective quality metric, visual quality, augmented reality, synthetic content, perceived quality

Cite: Christopher Haccius and Thorsten Herfet, "SC-VQM – A Visual Quality Metric for Synthetic Contents," International Journal of Signal Processing Systems, Vol. 5, No. 1, pp. 1-6, March 2017. doi: 10.18178/ijsps.5.1.1-6
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