Evaluation of Prediction of Quality Metrics for IR Images for UAV Applications

This study presents a framework to predict, in a No Reference (NR) manner, Full Reference (FR) objective quality metrics. The methods are applied to infrared (IR) images acquired by Unmanned Aerial Vehicle (UAV) and compressed on-board and then streamed to a ground computer. The proposed method computes two kinds of features, namely Bitstream Based (BB) features which are estimated from the H.264 bitstream and Pixel Based (PB) features which are estimated from the decoded images. Two BB features are computed using the H.264 Quantization Parameter (QP) and estimated PSNR [1]. A total of 53 PB features are calculated based on spatial information and the rest of the features are based on NR quality assessment methods [1, 2, 3]. The most relevant ones are selected and nally mapped to predict FR objective scores using Support Vector Regression. For the performance evaluation, the proposed method is trained to predict scores of 6 FR image quality metrics (SSIM, NQM, MSSIM, FSIM, MAD and PSNR-HMA) using a set of 250 IR aerial images compressed at 4 levels with H.264/AVC as I-frames. For the SVR mapping, 80% of the contents are used for training (200 contents or 800 images) and the remaining 200 images (20%) for testing. We have evaluated our model for three cases; all features, only BB features and finally excluding BB features. The average SROCC values obtained are 0.970, 0.962 and 0.943, respectively. The BB only version achieves very close results to that of using all features. Thus the presented NR BB Image Quality Assessment (IQA) method for the considered IR image material is very ecient. We have compared our method with three NR methods [1, 2, 3]. The proposed method is competitive compared to the state-of-the-art NR algorithms.