Nuke inpaint6/27/2023 ![]() An image inpainting technique based on the fast marching method. ACM Transactions on Graphics (TOG), 2014.Ī. Image completion using planar structure guidance. ACM Transactions on Graphics (TOG), 2009. PatchMatch: A randomized correspondence algorithm for structural image editing. Simoncelli Image quality assessment: from error visibility to structural similarity. Perceptually Motivated Benchmark for Video Matting. Mikhail Erofeev, Yury Gitman, Dmitriy Vatolin, Alexey Fedorov, Jue Wang. Fourth International Workshop on Quality of Multimedia Experience (QoMEX), pages 212–217, 2012. RMIT3DV: Pre-announcement of a creative commons uncompressed HD 3D video database. The test sequences with the respective completion masks are available for download: Deck, Library, Fountain, Wires, Tower, Skyscrapers, Sign.įor evaluation requests or if you have any questions or suggestions please feel free to contact us by email: EvaluationĮ. ![]() In cases where the developer specifically grants permission, we will publish the results on our site. We can evaluate the submitted data and report quality scores to the developer. We invite developers of video-completion methods to use our benchmark. Here Equation normal upper Omega Subscript p r e v Superscript w times w Baseline left-parenthesis x right-parenthesis denotes a square window of Equation w times w pixels (we use Equation w equal to 1/10th of the frame width) spatially centered at Equation x and located in the previous frame.Įxact computation of MS-C DSSIM and MS-C DSSIMdt quickly becomes impractical for larger spatio-temporal holes, so we resort to approximate solutions based on the PatchMatch algorithm. It is based on the structural similarity index (SSIM) values computed for all 9×9 luminance patches P(x) within the spatio-temporal hole Equation normal upper Omega. MS-DSSIM metric measures adherence of completion result V to ground truth video V ref in a multi-scale fashion with scale weights determined using perceptual quality data. Thorough description and comparative analysis of these and other metrics can be found in our paper (to be published soon). This benchmark employs four quality metrics: MS-DSSIM, MS-DSSIMdt, MS-C DSSIM, MS-C DSSIMdt. However, by relaxing the requirement of complete adherence to ground truth we can increase correlation with perceptual completion quality. It makes objective quality assessment of video completion an inherent problem. Video-completion results are seldom explicitly expected to adhere to ground truth and are usually judged only by their plausibility, which is assessed by a human observer. Each video-completion method takes the composited sequence and the corresponding object mask as input.Ī 3D model inserted in the background video using motion tracking to construct a test sequence with ground truth. ![]() To seamlessly insert a 3D model in a background video we use Blender motion-tracking tools. As foreground objects we use those employed in the video-matting benchmark as well as several 3D models. Some of these background videos include left-view sequences from the stereoscopic-video data set RMIT3dv. We consider object removal, so the test sequences are constructed by composing various foreground objects over a set of background videos. Our current data set consists of 7 video sequences with ground-truth completion results. We believe that our work can help rank existing methods and assist developers of new general-purpose video-completion methods. Additionally, we provide the results of an objective analysis using quality metrics that were carefully selected in our study on video-completion perceptual quality. ![]() We present results for different methods on a range of diverse test sequences which are available for viewing on a player equipped with a movable zoom region. The VideoCompletion project introduces the first benchmark for video-completion methods.
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