We propose a no-reference (NR) method for estimating the scores of metrics assessing the quality of infrared (IR) sequences compressed with H.264 for low-complexity unmanned aerial vehicle (UAV) applications. The scenario studied is to estimate the quality on an on-ground computer to avoid performing the processing onboard, due to the computational and memory limitations of the onboard hardware. For low complexity and fast feedback, a bitstream-based (BB) approach was chosen. The original IR sequences are captured by UAV, and then BB and pixel-based (PB) features are computed. Thereafter, a feature selection process is applied and the selected features are mapped using support vector regression, to predict the quality scores of full reference metrics. The method is evaluated for the NR prediction of four image and one video quality metrics. A set of five UAV- and three ground-IR sequences are used for evaluation. The proposed NR method consistently achieves robust results for the different objective metrics tested (Spearman rank-order correlation coefficients ranging from 0.91 to 0.99). A comparison with estimations based on features from three NR models from the literature proves to be in favor of the proposed method.
- Infrared image quality assessment
- Dynamic range reduction operator
- Machine learning