Multi-Operator Video Retargeting Method Based on Improved Seam Carving

Wang, Shuai; Tang, Zhenhua; Dong, Weixin; Yao, Jiemei

doi:10.1109/itoec49072.2020.9141774

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…The video aspect-ratio transformation algorithms of the literature can be divided in three main categories: i) warping [176,177], ii) cropping [178][179][180][181], and i) seam carving [182,183]. Warping methods, instead of resizing the entire video frame uniformly, determine scaling factors in a content-adaptive way: the frame is divided using a grid and important regions are left untouched, while scale factors are applied to other less-important areas.…”

Section: Transforming the Contentmentioning

confidence: 99%

Data-driven personalisation of television content: a survey

et al. 2022

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Section: Transforming the Contentmentioning

confidence: 99%

Data-driven personalisation of television content: a survey

et al. 2022

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“…The video aspect ratio transformation algorithms of the literature can be divided in three main categories: a) warping [1,2], b) cropping [3,4,5,6], and c) seam carving [7,8].…”

Section: Related Workmentioning

confidence: 99%

A Fast Smart-Cropping Method and Dataset for Video Retargeting

Apostolidis

Mezaris

2021

2021 IEEE International Conference on Image Processing (ICIP)

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In this paper a method that re-targets a video to a different aspect ratio using cropping is presented. We argue that cropping methods are more suitable for video aspect ratio transformation when the minimization of semantic distortions is a prerequisite. For our method, we utilize visual saliency to find the image regions of attention, and we employ a filteringthrough-clustering technique to select the main region of focus. We additionally introduce the first publicly available benchmark dataset for video cropping, annotated by 6 human subjects. Experimental evaluation on the introduced dataset shows the competitiveness of our method.

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“…As a result, the position of the impressive object may be misaligned. [2], which solves this problem, suppresses object misalignment by calculating the cost of seam changes that occur between consecutive frames. However, Shuai et al's method requires a large amount of processing time for the computation to suppress misalignment, and misalignment may occur when there are multiple impressive objects.…”

Section: Introductionmentioning

confidence: 99%

Fast seam carving for video with consideration of object misalignment suppression

Deguchi

Muraki

Awano

et al. 2023

International Workshop on Advanced Imaging Technology (IWAIT) 2023

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In recent years, with the spread of devices with various screen sizes, the demand for resizing methods is increasing. Seam carving (SC) is one of the resizing methods. This method removes visually unimportant areas (hereinafter referred to as "seam") and resizes the image without losing its impression. SC for video images has the problem that the most impressive areas are shifted because the seams are different from frame to frame. There are previous methods to solve this problem, but they require a huge amount of processing time. The proposed method enables SC with reduced misalignment and processing time by area segmentation and seam reuse.

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Multi-Operator Video Retargeting Method Based on Improved Seam Carving

Cited by 9 publications

References 14 publications

Data-driven personalisation of television content: a survey

Data-driven personalisation of television content: a survey

A Fast Smart-Cropping Method and Dataset for Video Retargeting

Fast seam carving for video with consideration of object misalignment suppression

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