A comprehensive review of image retargeting

被引:1
|
作者
Fan, Xiaoting [1 ]
Zhang, Zhong [1 ]
Sun, Long [2 ]
Xiao, Baihua [3 ]
Durrani, Tariq S. [4 ]
机构
[1] Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tra, Tianjin 300387, Peoples R China
[2] Tianjin Univ Commerce, Sch Informat Engn, Tianjin 300134, Peoples R China
[3] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[4] Univ Strathclyde, Dept Elect & Elect Engn, Glasgow, Scotland
基金
中国国家自然科学基金;
关键词
Image retargeting; Stereoscopic image; Discrete method; Continuous method; Deep learning; CONVOLUTIONAL NEURAL-NETWORK; SALIENT OBJECT DETECTION; QUALITY EVALUATION;
D O I
10.1016/j.neucom.2024.127416
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the development of display technologies, image retargeting plays a significant role in computer vision and pattern recognition communities currently. Image retargeting aims to display an image on a series of appliances with different resolutions and target aspect ratios. During the last decade, representative algorithms for image retargeting have been presented in the literature and achieved state-of-the-art performance. In this survey, we provide a comprehensive review of image retargeting, covering a wide variety of pioneering works for 2D image retargeting and stereoscopic image retargeting. 2D image retargeting focuses on preserving interesting regions when modifying the original image with arbitrary resolutions appropriately. Different from 2D image retargeting, stereoscopic image retargeting needs to preserve both the shape structure of salient objects and the depth consistency of 3D scenes simultaneously. In this survey, we start the first attempt to analyze the trends of 2D image retargeting and then summarize different types of stereoscopic image retargeting. Secondly, image retargeting quality assessment metrics for 2D images and stereoscopic images are introduced to evaluate retargeted images. Thirdly, we also investigate the evaluation datasets, and give the comparison results and analysis between different representative methods. Finally, the promising future research is thoroughly discussed to further improve the performance of 2D and stereoscopic image retargeting.
引用
收藏
页数:18
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