Video retargeting through spatio‐temporal seam carving using Kalman filter

Kaur, Harpreet; Kour, Swarnjeet; Sen, Debashis

doi:10.1049/iet-ipr.2019.0236

“…[138] optimizes backward and forward energy jointly, and proposes isosurface protection and to encode the opacity transfer function. Kaur et al [139] use Kalman filter to optimize the seam carving, which is theoretical simple and hardware friendly. Hsin et al [140] manipulate saliency histogram, and propose saliency histogram equalisation-seam carving (SHE-SC) algorithm.…”

Section: Seam Carvingmentioning

confidence: 99%

AI Video Editing: a Survey

Zhang¹,

Li²,

Han³

et al. 2022

Preprint

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Video editing is a high-required job, for it requires skilled artists or workers equipped with plentiful physical strength and multidisciplinary knowledge, such as cinematography, aesthetics. Thus gradually, more and more researches focus on proposing semi-automatical and even fully automatical solutions to reduce workloads. Since those conventional methods are usually designed to follow some simple guidelines, they lack flexibility and capability to learn complex ones. Fortunately, the advances of computer vision and machine learning make up the shortages of traditional approaches and make AI editing feasible. There is no survey to conclude those emerging researches yet. This paper summaries the development history of automatic video editing, and especially the applications of AI in partial and full workflows. We emphasizes video editing and discuss related works from multiple aspects: modality, type of input videos, methology, optimization, dataset, and evaluation metric. Besides, we also summarize the progresses in image editing domain, i.e., style transferring, retargeting, and colorization, and seek for the possibility to transfer those techniques to video domain. Finally, we give a brief conclusion about this survey and explore some open problems.

show abstract

“…[138] optimizes backwardbackwardsward energy jointly, proposes isosurface protection and encode the opacity transfer function. Kaur et al [139] use Kalman filter to optimize the seam carving, which is theoretical simple and hardware friendly. Hsin et al [140] manipulate saliency histogram, and propose saliency histogram equalisationseam carving (SHE-SC) algorithm.…”

Section: Seam Carvingmentioning

confidence: 99%

AI Video Editing: a Survey

Zhang¹,

Li²,

Han³

et al. 2022

Preprint

View full text Add to dashboard Cite

Video editing is a high-required job, for it requires skilled artists or workers equipped with plentiful physical strength and multidisciplinary knowledge, such as cinematography, aesthetics. Thus gradually, more and more researches focus on proposing semi-automatical and even fully automatical solutions to reduce workloads. Since those conventional methods are usually designed to follow some simple guidelines, they lack flexibility and capability to learn complex ones. Fortunately, the advances of computer vision and machine learning make up the shortages of traditional approaches and make AI editing feasible. There is no survey to conclude those emerging researches yet. This paper summaries the development history of automatic video editing, and especially the applications of AI in partial and full workflows. We emphasizes video editing and discuss related works from multiple aspects: modality, type of input videos, methology, optimization, dataset, and evaluation metric. Besides, we also summarize the progresses in image editing domain, i.e., style transferring, retargeting, and colorization, and seek for the possibility to transfer those techniques to video domain. Finally, we give a brief conclusion about this survey and explore some open problems.

show abstract

“…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%