A system for retargeting of streaming video

Krähenbühl, Philipp; Lang, Manuel; Hornung, Alexander; Groß, Markus

doi:10.1145/1661412.1618472

Cited by 134 publications

(140 citation statements)

References 26 publications

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“…Two more comparisons including stream video [11] and multi-operator [16] are shown in Fig.1. As expected, our method achieves the best overall balance between retaining the prominent contents and minimizing the distortion on image structure.…”

Section: Resultsmentioning

confidence: 99%

“…Image retargeting by improved seam carving [15], scale-and-stretch [22], stream video [11], shift map [13], multi-operator [16], and our method. Note the distorted flags and chairs by [15,11], unnatural stretch by [22,11], and cut on humans and misplacement on roof and chairs by [13]. The results by [16] are close to ours, yet ours tend to better retain prominent areas while minimizing the distortion.…”

Section: Stream Video Original Imagementioning

confidence: 99%

“…Our method significantly differs from existing contentaware retargeting approaches [1,15,22,23,16,11,24]. First and most importantly, we for the first time use the original image directly to constrain the retargeting manipulation.…”

Section: Stream Video Original Imagementioning

confidence: 99%

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Importance filtering for image retargeting

Ding

Xiao

2011

CVPR 2011

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Section: Resultsmentioning

confidence: 99%

Section: Stream Video Original Imagementioning

confidence: 99%

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Importance filtering for image retargeting

Ding

Xiao

2011

CVPR 2011

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“…The warping technique by Krähenbühl et al [44] works again in real-time and is designed for the retargeting of streaming videos. Instead of using a mesh to divide the frames into regions, the warp is computed per-pixel.…”

Section: Continuous Methodsmentioning

confidence: 99%

Algorithms for video retargeting

Kopf

Haenselmann

Kiess

et al. 2011

Multimed Tools Appl

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The visualization of high resolution video on small mobile devices is still a great challenge today. Most critical are the limited display resolution and different aspect ratios of handheld mobile devices. So far, there is no retargeting algorithm available that guarantees good results for all videos. We introduce a new video retargeting approach that reduces the resolution while preserving as much of the relevant content as possible. A central component of the system selects the most suitable algorithm to adapt a given shot. We have implemented two retargeting algorithms: a region of interest (ROI) based technique, and a fast implementation of seam carving for size adaptation of videos (FSCAV). The ROI-based retargeting detects important regions like faces, objects, text, and contrast-based saliency regions. A rectangular window within the larger frame is selected that defines the visible area of the target video. If several relevant regions are detected, an artificial camera motion (pan, tilt, or zoom) may change the selected view within a shot. For seam carving, we present two extensions: The first reduces the distortion of straight lines (lines may become curved or disconnected); the second avoids jitter in the target video, limits the large memory requirements and computational effort of seam carving, and makes it applicable to video retargeting. In addition, we present a heuristic that estimates the visual quality of the target video. If the quality drops below a threshold, the ROI-based retargeting is used for this shot. User evaluations confirm a very high visual quality of our approach.

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“…1. If viewers specify the ROI (region of interest), the significant areas also can be propagated automatically into neighboring frames [32].…”

Section: Mesh Deformation Energymentioning

confidence: 99%

A retargeting method for stereoscopic 3D video

Liu

Sun

2015

Comp. Visual Media

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We propose a disparity-constrained retargeting method for stereoscopic 3D video, which simultaneously resizes a binocular video to a new aspect ratio and remaps the depth to the perceptual comfort zone. First, we model distortion energies to prevent important video contents from deforming. Then, to maintain depth mapping stability, we model disparity variation energies to constraint the disparity range both in spatial and temporal domains. The last component of our method is a non-uniform, pixel-wise warp to the target resolution based on these energy models. Using this method, we can process the original stereoscopic video to generate new, high-perceptual-quality versions at different display resolutions. For evaluation, we conduct a user study; we also discuss the performance of our method.

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A system for retargeting of streaming video

Cited by 134 publications

References 26 publications

Importance filtering for image retargeting

Importance filtering for image retargeting

Algorithms for video retargeting

A retargeting method for stereoscopic 3D video

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