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A system for retargeting of streaming video

机译:用于流媒体视频重新定位的系统

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We present a novel, integrated system for content-aware video retargeting. A simple and interactive framework combines key frame based constraint editing with numerous automatic algorithms for video analysis. This combination gives content producers high level control of the retargeting process. The central component of our framework is a non-uniform, pixel-accurate warp to the target resolution which considers automatic as well as interactively defined features. Automatic features comprise video saliency, edge preservation at the pixel resolution, and scene cut detection to enforce bilateral temporal coherence. Additional high level constraints can be added by the producer to guarantee a consistent scene composition across arbitrary output formats. For high quality video display we adopted a 2D version of EWA splatting eliminating aliasing artifacts known from previous work. Our method seamlessly integrates into postproduction and computes the reformatting in real-time. This allows us to retarget annotated video streams at a high quality to arbitary aspect ratios while retaining the intended cinematographic scene composition. For evaluation we conducted a user study which revealed a strong viewer preference for our method.
机译:我们提出了一种用于内容感知视频重新定位的新型集成系统。一种简单互动的框架,将基于键帧的约束编辑与许多自动算法进行视频分析。这种组合为内容生产商提供了高级控制的复靶过程。我们的框架的中央组件是一种不均匀的像素准确的翘曲,其对目标分辨率进行了自动以及相互作用的定义功能。自动特征包括像素分辨率的视频显着性,边缘保存,以及场景剪裁检测,以实施双边时间相干性。生产者可以添加额外的高级约束,以保证跨任意输出格式的一致场景组成。对于高质量的视频显示,我们采用了2D版EWA Splatting,消除了以前的工作中已知的别名伪像。我们的方法无缝地集成到后生产,并在实时计算重新格式化。这允许我们以高质量的高质量来重新靶向被带动的视频流,同时保留预期的电影场景组成。对于评估,我们进行了一项用户学习,揭示了对我们方法的强烈观众偏好。

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