A system for retargeting of streaming video

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

doi:10.1145/1618452.1618472

Cited by 158 publications

(116 citation statements)

References 30 publications

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“…(18) As can be seen, there exist curving artifacts at the right border of the resized frame in the warp method. (18) They are absent in the image resized using the proposed algorithm. Figure 5 presents visual comparison with the improved SC.…”

Section: Resultsmentioning

confidence: 97%

“…The extended SH-based video resizing algorithm was compared with other existing algorithms. (18,19) The discrete cosine transform (DCT)-based pairwise comparison method (16) was used for scene change detection. Different scenes were resized separately as temporal coherence is not necessary for disjointed scenes.…”

Section: Resultsmentioning

confidence: 99%

“…(18,19) As the warping approach is mainly dependent on the estimation of SH, there is a possibility of overwarping in regions with low saliency. One example is given in Fig.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Image Resizing in Saliency Histogram Domain

2017

Sensors and Materials

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The saliency map of an image provides useful information regarding the region of interest. In this paper, the normalized saliency map, called the saliency histogram, is used as a valid probability density function, based on which an efficient algorithm has been proposed for image resizing in the saliency histogram domain. Moreover, the saliency-histogram-based image resizing algorithm has been extended to video applications. Experimental results show the performance of the saliency histogram in terms of the preservation of salient objects in resized images and videos. The proposed approach is suitable for content-driven surveillance with multiresolution image sensor systems.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

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Image Resizing in Saliency Histogram Domain

2017

Sensors and Materials

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“…To solve these types of visual artifacts, several recent CPW methods regarding transformation of one image consider line preserving in their objective functions. In [11,12], the authors maintained the slope of lines by casting constraints on the points on the detected lines to maintain the direction of the lines. In [13,14], the authors grouped lines according to their Hough angle and applied the identical rotation to lines falling in the same group.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective content preserving warping for image stitching

Zhang

et al. 2015

2015 IEEE International Conference on Multimedia and Expo (ICME)

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Image stitching has been developed to enhance users' immersive experiences by constructing new images of higher spatial resolution and broader field of view from multiple input images. Most image stitching schemes assume 2D transformation of the whole frame between images. There usually exist local misalignments of contents that do not match with the transformation and may cause artifacts such as ghosting. Several schemes including seam cutting, image blending and other image composition algorithms, have been developed to relieve such artifacts. However, these schemes are essentially post-stitching processing that do not address the alignment problem and would fail when the parallax is large. In this paper, we propose a multi-objective content preserving warping (CPW) for local adjustment of image alignment which is optimized considering feature matching, line preserving, line matching, shape smoothness and other factors to significantly reduce the visual artifacts. In particular, we adjust the image warping from their pre-warping results using the homography estimated during the image registration step. Experimental results show that the proposed CPW scheme for image stitching is able to generate much smoother stitching results comparing with the state-of-the-art methods reported in the literature.

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“…The warping algorithm proposed by Krähenbühl et al [6] performs a warp on the pixel level and for each frame separately. In order to preserve temporal coherence, the perframe importance maps are combined with a lookahead of a few frames.…”

Section: Related Workmentioning

confidence: 99%

SeamCrop

Kiess

Guthier

Kopf

et al. 2012

Proceedings of the 4th Workshop on Mobile Video

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The resizing of videos is currently in high demand due to the popularity of mobile devices with very small screen sizes. As the screen resolution of these devices increases, the resizing is mostly needed to change the aspect ratio of the videos to fit the display size. In this paper we present a novel and fast approach for the retargeting of videos which combines cropping and seam carving. The idea is to find an optimal cropping window and to get more useful content into it by additionally removing seams. We formulate the search for a cropping window in terms of a 2D rectangle representing the possible positions of the window over all frames. Finding the optimal position can be solved efficiently with dynamic programming. A user study was conducted that compares our approach to a similar state-of-the-art video retargeting technique, showing that our new algorithm has fast computation times while also having a comparable quality of results.

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A system for retargeting of streaming video

Cited by 158 publications

References 30 publications

Image Resizing in Saliency Histogram Domain

Image Resizing in Saliency Histogram Domain

Multi-objective content preserving warping for image stitching

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