TY - GEN
T1 - Registration based retargeted image quality assessment
AU - Zhang, Bo
AU - Sander, Pedro V.
AU - Bermak, Amine
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - In recent years, a large number of image retargeting methods have been proposed. Measuring their relative quality is of significant importance, and there is still room for improvement in the effectiveness of objective retargeted image quality assessment (RIQA) metrics. In this paper, we propose a registration based RIQA metric. First, we propose to calculate the flow map using an image registration method which involves SURF point matching and halfway domain optimization. Using the computed flow map and the source image, we propose an LGI metric which contains three factors: 1) local similarity which assesses the local aspect ratio change, edge directional similarity and flow smoothness; 2) global distortion which measures the appearance change of salient objects; 3) salient information loss. Comparing with other six metrics, our LGI metric correlates the best with subjective rankings on the RetargetMe dataset.
AB - In recent years, a large number of image retargeting methods have been proposed. Measuring their relative quality is of significant importance, and there is still room for improvement in the effectiveness of objective retargeted image quality assessment (RIQA) metrics. In this paper, we propose a registration based RIQA metric. First, we propose to calculate the flow map using an image registration method which involves SURF point matching and halfway domain optimization. Using the computed flow map and the source image, we propose an LGI metric which contains three factors: 1) local similarity which assesses the local aspect ratio change, edge directional similarity and flow smoothness; 2) global distortion which measures the appearance change of salient objects; 3) salient information loss. Comparing with other six metrics, our LGI metric correlates the best with subjective rankings on the RetargetMe dataset.
KW - dense correspondence
KW - edge direction similarity
KW - retargeted image quality assessment (RIQA)
KW - salient object segmentation
UR - http://www.scopus.com/inward/record.url?scp=85023740437&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952358
DO - 10.1109/ICASSP.2017.7952358
M3 - Conference contribution
AN - SCOPUS:85023740437
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1258
EP - 1262
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 -