Abstract
Image retargeting aims at displaying an image on a serious of device screen with different sizes, which has been widely applied in computer graphics and other fields. At present, many deep learning-based image retargeting methods implement an encoder-decoder retargeting network to resize attention map. Then, various types of loss functions are presented to preserve salient contents and reduce structure distortions. In this survey, we first review three types of loss functions utilized in deep image retargeting that consists of pixel-based, probability-based, and perception-based. Furthermore, we explore the baseline encoder-decoder retargeting network with three types of loss functions, and conduct the experiments on two public datasets, Retargetme and Pascal Voc 2007 datasets to verify their impact on the deep image retargeting.
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Acknowledgements
This work was supported in part by the Natural Science Foundation of Tianjin under Grant No. 22JCQNJC00010, in part by the Scientific Research Project of Tianjin Educational Committee under Grant No. 2022KJ011, and in part by the National Natural Science Foundation of China under Grant No. 62171321.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Fan, X., Sun, L., Zhang, Z. (2024). Deep Image Retargeting Network with Multi-loss Functions. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1032. Springer, Singapore. https://doi.org/10.1007/978-981-99-7505-1_57
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DOI: https://doi.org/10.1007/978-981-99-7505-1_57
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