Learning a blind measure of perceptual image quality
| Autor: | Neel Joshi, Huixuan Tang, Ashish Kapoor |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
business.industry
Image quality Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Automatic image annotation Kernel (image processing) Image texture Feature (computer vision) Histogram Computer vision Perceptual image quality Artificial intelligence business Transform coding Feature detection (computer vision) |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2011.5995446 |
| Popis: | It is often desirable to evaluate an image based on its quality. For many computer vision applications, a perceptually meaningful measure is the most relevant for evaluation; however, most commonly used measure do not map well to human judgements of image quality. A further complication of many existing image measure is that they require a reference image, which is often not available in practice. In this paper, we present a “blind” image quality measure, where potentially neither the groundtruth image nor the degradation process are known. Our method uses a set of novel low-level image features in a machine learning framework to learn a mapping from these features to subjective image quality scores. The image quality features stem from natural image measure and texture statistics. Experiments on a standard image quality benchmark dataset shows that our method outperforms the current state of art. |
| Databáze: | OpenAIRE |
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