Improving Robustness of Image Quality Measurement with Degradation Classification and Machine Learning

Image quality metrics can be classified as generic or degradation specific. Degradation specific measures perform poorly under "mismatched" conditions. Generic measures, on the other hand, may compromise quality measurement accuracy while gaining robustness to variation in distortion conditions. To improve the accuracy-robustness tradeoff, we employ support-vector degradation classification and machine learning tools to judiciously combine generic and degradation specific measures. To test our algorithm, composite quality metrics are optimized for five different distortion classes. Experiment results show that the proposed algorithm achieves improved performance and robustness relative to two benchmark generic quality metrics.