Fast Multi-Operator Image Resizing and Evaluation

Dong, Weiming; Bao, Guanbo; Zhang, Xiao-Peng; Paul, Jean-Claude

doi:10.1007/s11390-012-1211-6

Cited by 40 publications

(37 citation statements)

References 30 publications

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“…Based on the results in Fig. 4, comparing the results of image downsizing to 75 % width of the original image, it is clear that our method preserves the global structure better than other three approaches including cropping, SNS [4] and FMO method [9]. 4 …”

Section: Resultsmentioning

confidence: 89%

“…The retargeted results obtained by our improved resizing method are compared against those of ISC [2], as well as those of [4,7,8,9]. Figures in this paper show that outputs of our algorithm are more pleasant in visual.…”

Section: Resultsmentioning

confidence: 99%

“…The criterion is tested empirically, the coefficient 3/4 may be justified. [2],(c) SNS [4] ,(d) FMO [9] and (e) Our algorithm.…”

Section: Two-dimension Seam Searchmentioning

confidence: 99%

“…This saliency detector was used in [8] to compute the importance value of the pixel replacing the gradient map for seam carving. Dong et al [9] developed a fast multi-operator image resizing method (FMO) combining seam carving with scaling and cropping. The speed of algorithm is fast in a sense while the resizing results may be unsatisfactory.…”

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confidence: 99%

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Improved Image Resizing using Seam Carving and scaling

Zhang¹,

Jonathan²,

Peng³

2013

Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)

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Abstr act. Seam Carving, the popular content-aware image resizing technique, removes or inserts seams of low energy iteratively without sufficiently considering their impact on the global visual quality of the image. However, sometimes seams pass through the regions of interest (ROIs) with low energy and distort the geometric shapes of the important objects. In this paper, we propose an improved resizing method based on improved seam carving method that can prevent the seams going through the ROIs and better preserve the content of the image. With considering frequency-tuned saliency map and the distance to the image center for each pixel in the importance map, it can protect the ROIs when the image is resized to the target size. Moreover, a developed two-dimension search scheme for seam carving and an switching scheme between seam caving and scaling are also proposed to efficiently protect the global visual of the image. Experiments demonstrate that resizing results of the proposed method are more pleasant than those of cropping, uniform scaling and other several methods.Keywor ds: image resizing • saliency map • center distance map • twodimension search 1 Intr oduction With the development of digital media technology, it is popular to browse images on the various display devices of different resolutions and sizes such as mobile phones, cameras, televisions. How can we provide as good as possible visual

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

“…The criterion is tested empirically, the coefficient 3/4 may be justified. [2],(c) SNS [4] ,(d) FMO [9] and (e) Our algorithm.…”

Section: Two-dimension Seam Searchmentioning

confidence: 99%

mentioning

confidence: 99%

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Improved Image Resizing using Seam Carving and scaling

Zhang¹,

Jonathan²,

Peng³

2013

Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)

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“…While some rely on the importance measure for switching [12,23,29], Dong et al present algorithms using an image distance function [13] or an operator cost-based approach [14]. The main difficulty when using hybrid methods is to find the optimal switching point between the operators as each viewer might prefer a different combination [67].…”

Section: Hybrid 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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