TY - GEN
T1 - Statistics of natural fused image distortions
AU - Moreno-Villamarin, David E.
AU - Benitez-Restrepo, Hernan D.
AU - Bovik, Alan C.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - The capability to automatically evaluate the quality of long wave infrared (LWIR) and visible light images has the potential to play an important role in determining and controlling the quality of a resulting fused LWIR-visible image. Extensive work has been conducted on studying the statistics of natural LWIR and visible light images. Nonetheless, there has been little work done on analyzing the statistics of fused images and associated distortions. In this paper, we study the natural scene statistics (NSS) of fused images and how they are affected by several common types of distortions, including blur, white noise, JPEG compression, and non-uniformity (NU). Based on the results of a separate subjective study on the quality of pristine and degraded fused images, we propose an opinion-aware (OA) fused image quality analyzer, whose relative predictions with respect to other state-of-the-art metrics correlate better with human perceptual evaluations.
AB - The capability to automatically evaluate the quality of long wave infrared (LWIR) and visible light images has the potential to play an important role in determining and controlling the quality of a resulting fused LWIR-visible image. Extensive work has been conducted on studying the statistics of natural LWIR and visible light images. Nonetheless, there has been little work done on analyzing the statistics of fused images and associated distortions. In this paper, we study the natural scene statistics (NSS) of fused images and how they are affected by several common types of distortions, including blur, white noise, JPEG compression, and non-uniformity (NU). Based on the results of a separate subjective study on the quality of pristine and degraded fused images, we propose an opinion-aware (OA) fused image quality analyzer, whose relative predictions with respect to other state-of-the-art metrics correlate better with human perceptual evaluations.
KW - LWIR
KW - NSS
KW - fusion performance
KW - image quality
KW - multi-resolution image fusion
UR - http://www.scopus.com/inward/record.url?scp=85021759126&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952355
DO - 10.1109/ICASSP.2017.7952355
M3 - Conference contribution
AN - SCOPUS:85021759126
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1243
EP - 1247
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -