79671 20190819103726.0 doi 10.1109/TVCG.2016.2517641 sideral 97877 ART-2017-97877 eng Liang, Yun Objective quality prediction of image retargeting algorithms 2017 Access copy available to the general public Unrestricted Quality assessment of image retargeting results is useful when comparing different methods. However, performing the necessary user studies is a long, cumbersome process. In this paper, we propose a simple yet efficient objective quality assessment method based on five key factors: i) preservation of salient regions; ii) analysis of the influence of artifacts; iii) preservation of the global structure of the image; iv) compliance with well-established aesthetics rules; and v) preservation of symmetry. Experiments on the RetargetMe benchmark, as well as a comprehensive additional user study, demonstrate that our proposed objective quality assessment method outperforms other existing metrics, while correlating better with human judgements. This makes our metric a good predictor of subjective preference. info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 3.078 2017 COMPUTER SCIENCE, SOFTWARE ENGINEERING 8 / 104 = 0.077 2017 Q1 T1 0.869 2017 Computer Graphics and Computer-Aided Design 2017 Q1 Software 2017 Q1 Signal Processing 2017 Q1 Computer Vision and Pattern Recognition 2017 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Liu, Yong Lin (orcid)0000-0002-7503-7022 Gutierrez, Diego Universidad de Zaragoza 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 23, 2 (2017), 1099-1110 IEEE trans. vis. comput. graph. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 1077-2626 716420 http://zaguan.unizar.es/record/79671/files/texto_completo.pdf Postprint 13524 http://zaguan.unizar.es/record/79671/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:79671 articulos driver 2019-08-19-09:43:00 ARTICLE