Consistent Video Filtering for Camera Arrays
Visual formats have advanced beyond single‐view images and videos: 3D movies are commonplace, researchers have developed multi‐view navigation systems, and VR is helping to push light field cameras to mass market. However, editing tools for these media are still nascent, and even simple filtering op...
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          | Published in | Computer graphics forum Vol. 36; no. 2; pp. 397 - 407 | 
|---|---|
| Main Authors | , , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Oxford
          Blackwell Publishing Ltd
    
        01.05.2017
     Wiley  | 
| Series | Proc. Eurographics 2017 | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 0167-7055 1467-8659  | 
| DOI | 10.1111/cgf.13135 | 
Cover
| Abstract | Visual formats have advanced beyond single‐view images and videos: 3D movies are commonplace, researchers have developed multi‐view navigation systems, and VR is helping to push light field cameras to mass market. However, editing tools for these media are still nascent, and even simple filtering operations like color correction or stylization are problematic: naively applying image filters per frame or per view rarely produces satisfying results due to time and space inconsistencies. Our method preserves and stabilizes filter effects while being agnostic to the inner working of the filter. It captures filter effects in the gradient domain, then uses input frame gradients as a reference to impose temporal and spatial consistency. Our least‐squares formulation adds minimal overhead compared to naive data processing. Further, when filter cost is high, we introduce a filter transfer strategy that reduces the number of per‐frame filtering computations by an order of magnitude, with only a small reduction in visual quality. We demonstrate our algorithm on several camera array formats including stereo videos, light fields, and wide baselines. | 
    
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| AbstractList | Visual formats have advanced beyond single-view images and videos: 3D movies are commonplace, researchers have developed multi-view navigation systems, and VR is helping to push light field cameras to mass market. However, editing tools for these media are still nascent, and even simple filtering operations like color correction or stylization are problematic: naively applying image filters per frame or per view rarely produces satisfying results due to time and space inconsistencies. Our method preserves and stabilizes filter effects while being agnostic to the inner working of the filter. It captures filter effects in the gradient domain, then uses \emph{input} frame gradients as a reference to impose temporal and spatial consistency. Our least-squares formulation adds minimal overhead compared to naive data processing. Further, when filter cost is high, we introduce a filter transfer strategy that reduces the number of per-frame filtering computations by an order of magnitude, with only a small reduction in visual quality. We demonstrate our algorithm on several camera array formats including stereo videos, light fields, and wide baselines. Visual formats have advanced beyond single‐view images and videos: 3D movies are commonplace, researchers have developed multi‐view navigation systems, and VR is helping to push light field cameras to mass market. However, editing tools for these media are still nascent, and even simple filtering operations like color correction or stylization are problematic: naively applying image filters per frame or per view rarely produces satisfying results due to time and space inconsistencies. Our method preserves and stabilizes filter effects while being agnostic to the inner working of the filter. It captures filter effects in the gradient domain, then uses input frame gradients as a reference to impose temporal and spatial consistency. Our least‐squares formulation adds minimal overhead compared to naive data processing. Further, when filter cost is high, we introduce a filter transfer strategy that reduces the number of per‐frame filtering computations by an order of magnitude, with only a small reduction in visual quality. We demonstrate our algorithm on several camera array formats including stereo videos, light fields, and wide baselines.  | 
    
| Author | Pfister, Hanspeter Bonneel, Nicolas Tompkin, James Sun, Deqing Wang, Oliver Sunkavalli, Kalyan Paris, Sylvain  | 
    
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| Copyright | 2017 The Author(s) Computer Graphics Forum © 2017 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2017 The Eurographics Association and John Wiley & Sons Ltd. Distributed under a Creative Commons Attribution 4.0 International License  | 
    
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| Keywords | Video Processing Filtering Algorithm Video 2D and 3D  | 
    
| Language | English | 
    
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| Snippet | Visual formats have advanced beyond single‐view images and videos: 3D movies are commonplace, researchers have developed multi‐view navigation systems, and VR... Visual formats have advanced beyond single-view images and videos: 3D movies are commonplace, researchers have developed multi-view navigation systems, and VR...  | 
    
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| SubjectTerms | 3-D films Cameras Categories and Subject Descriptors (according to ACM CCS) Computer Science Data processing Digital video Field cameras Graphics I.2.10 [Computer Graphics]: Vision and Scene Understanding—Video analysis I.4.3 [Computer Graphics]: Enhancement—Filtering Image filters Image Processing Image processing systems Navigation systems  | 
    
| Title | Consistent Video Filtering for Camera Arrays | 
    
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