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Algorithms for video retargeting

delete2011-01-07
delete23
PRE
AI
S
Stephan Kopf *
T
Thomas Haenselmann
J
Johannes Kiess
B
Benjamin Guthier
W
Wolfgang Effelsberg
DOI:10.1007/s11042-010-0717-6delete
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Abstract

Abstract

En 中文
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.
Keywords:
Video retargeting
Video adaptation
Seam carving
Region of interest
Contrast-based saliency
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Journal

Multimedia Tools and Applications cover
Multimedia Tools and Applications
IF:
3
Papers:
1.9W
Citations:
3.2W

Organization

U
University of Mannheim
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1.9K
Papers: 2.2K
Citations: 3.2K