Blind video temporal consistency

Extending image processing techniques to videos is a non-trivial task; applying processing independently to each video frame often leads to temporal inconsistencies, and explicitly encoding temporal consistency requires algorithmic changes. We describe a more general approach to temporal consistency. We propose a gradient-domain technique that is blind to the particular image processing algorithm. Our technique takes a series of processed frames that suffers from flickering and generates a temporally-consistent video sequence. The core of our solution is to infer the temporal regularity from the original unprocessed video, and use it as a temporal consistency guide to stabilize the processed sequence. We formally characterize the frequency properties of our technique, and demonstrate, in practice, its ability to stabilize a wide range of popular image processing techniques including enhancement and stylization of color and tone, intrinsic images, and depth estimation.

[1]  Patrick Pérez,et al.  Poisson image editing , 2003, ACM Trans. Graph..

[2]  Michael F. Cohen,et al.  Fourier Analysis of the 2D Screened Poisson Equation for Gradient Domain Problems , 2008, ECCV.

[3]  Rob Fergus,et al.  Depth Map Prediction from a Single Image using a Multi-Scale Deep Network , 2014, NIPS.

[4]  Alan L. Yuille,et al.  Region-based temporally consistent video post-processing , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Alexei A. Efros,et al.  Fast bilateral filtering for the display of high-dynamic-range images , 2002 .

[6]  Peter V. Gehler,et al.  Intrinsic Video , 2014, ECCV.

[7]  Joost van de Weijer,et al.  Generalized Gamut Mapping using Image Derivative Structures for Color Constancy , 2008, International Journal of Computer Vision.

[8]  Mohinder Malhotra Single Image Haze Removal Using Dark Channel Prior , 2016 .

[9]  Ketan Tang,et al.  Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Holger Winnemöller,et al.  Real-time video abstraction , 2006, SIGGRAPH 2006.

[11]  Ce Liu,et al.  Exploring new representations and applications for motion analysis , 2009 .

[12]  Eli Shechtman,et al.  Patch-based high dynamic range video , 2013, ACM Trans. Graph..

[13]  Sylvain Paris,et al.  Edge-Preserving Smoothing and Mean-Shift Segmentation of Video Streams , 2008, ECCV.

[14]  Stefan Gustavson,et al.  Unified HDR reconstruction from raw CFA data , 2013, IEEE International Conference on Computational Photography (ICCP).

[15]  Adam Finkelstein,et al.  PatchMatch: a randomized correspondence algorithm for structural image editing , 2009, SIGGRAPH 2009.

[16]  Dani Lischinski,et al.  Gradient Domain High Dynamic Range Compression , 2023 .

[17]  英樹 藤堂,et al.  Interactive intrinsic video editing , 2014, ACM Trans. Graph..

[18]  Joost van de Weijer,et al.  Improving Color Constancy by Photometric Edge Weighting , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Joan Sol Roo,et al.  Temporally coherent video de-anaglyph , 2014, SIGGRAPH '14.

[20]  Jiawen Chen,et al.  Real-time edge-aware image processing with the bilateral grid , 2007, SIGGRAPH 2007.

[21]  Sylvain Paris,et al.  Example-based video color grading , 2013, ACM Trans. Graph..

[22]  P.M.B. Van Roosmalen,et al.  Restoration of archived film and video , 1999 .

[23]  Horst Bischof,et al.  Motion estimation with non-local total variation regularization , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  Aljoscha Smolic,et al.  Suplemental Material for Temporally Coherent Local Tone Mapping of HDR Video , 2014 .

[25]  Yao-Hsien Huang,et al.  An effective algorithm for image sequence color transfer , 2006, Math. Comput. Model..

[26]  Zeev Farbman,et al.  Tonal stabilization of video , 2011, SIGGRAPH 2011.

[27]  Michael F. Cohen,et al.  GradientShop: A gradient-domain optimization framework for image and video filtering , 2010, TOGS.

[28]  James H. Elder,et al.  Are Edges Incomplete? , 1999, International Journal of Computer Vision.

[29]  Dani Lischinski,et al.  Non-rigid dense correspondence with applications for image enhancement , 2011, ACM Trans. Graph..

[30]  Michael J. Black,et al.  A Naturalistic Open Source Movie for Optical Flow Evaluation , 2012, ECCV.

[31]  R. Weinstock Calculus of Variations: with Applications to Physics and Engineering , 1952 .

[32]  Stephen Lin,et al.  A Closed-Form Solution to Retinex with Nonlocal Texture Constraints , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Julien Rabin,et al.  Sliced and Radon Wasserstein Barycenters of Measures , 2014, Journal of Mathematical Imaging and Vision.

[34]  Jan Kautz,et al.  Local Laplacian filters: edge-aware image processing with a Laplacian pyramid , 2011, ACM Trans. Graph..

[35]  Frédo Durand,et al.  Fast Local Laplacian Filters , 2014, ACM Trans. Graph..

[36]  Andrew W. Fitzgibbon,et al.  PMBP: PatchMatch Belief Propagation for Correspondence Field Estimation , 2014, International Journal of Computer Vision.

[37]  Markus Gross,et al.  Practical temporal consistency for image-based graphics applications , 2012, ACM Trans. Graph..

[38]  Frédo Durand,et al.  Light mixture estimation for spatially varying white balance , 2008, ACM Trans. Graph..

[39]  Jiawen Chen,et al.  Real-time edge-aware image processing with the bilateral grid , 2007, ACM Trans. Graph..

[40]  Anil C. Kokaram,et al.  A New Robust Technique for Stabilizing Brightness Fluctuations in Image Sequences , 2004, ECCV Workshop SMVP.

[41]  Julie Delon,et al.  Stabilization of Flicker-Like Effects in Image Sequences through Local Contrast Correction , 2010, SIAM J. Imaging Sci..

[42]  Qionghai Dai,et al.  Intrinsic video and applications , 2014, ACM Trans. Graph..

[43]  Anil Kokaram,et al.  LOCALISED DEFLICKER OF MOVING IMAGES , 2006 .

[44]  Michael J. Black,et al.  A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them , 2013, International Journal of Computer Vision.

[45]  Michael J. Black,et al.  Efficient sparse-to-dense optical flow estimation using a learned basis and layers , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[46]  Noah Snavely,et al.  Intrinsic images in the wild , 2014, ACM Trans. Graph..

[47]  Deepu Rajan,et al.  Improving Image Matting Using Comprehensive Sampling Sets , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.