Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Learning Spatio-Temporal Downsampling for Effective Video Upscaling

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13678))

Included in the following conference series:

Abstract

Downsampling is one of the most basic image processing operations. Improper spatio-temporal downsampling applied on videos can cause aliasing issues such as moiré patterns in space and the wagon-wheel effect in time. Consequently, the inverse task of upscaling a low-resolution, low frame-rate video in space and time becomes a challenging ill-posed problem due to information loss and aliasing artifacts. In this paper, we aim to solve the space-time aliasing problem by learning a spatio-temporal downsampler. Towards this goal, we propose a neural network framework that jointly learns spatio-temporal downsampling and upsampling. It enables the downsampler to retain the key patterns of the original video and maximizes the reconstruction performance of the upsampler. To make the downsamping results compatible with popular image and video storage formats, the downsampling results are encoded to uint8 with a differentiable quantization layer. To fully utilize the space-time correspondences, we propose two novel modules for explicit temporal propagation and space-time feature rearrangement. Experimental results show that our proposed method significantly boosts the space-time reconstruction quality by preserving spatial textures and motion patterns in both downsampling and upscaling. Moreover, our framework enables a variety of applications, including arbitrary video resampling, blurry frame reconstruction, and efficient video storage.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Allebach, J., Wong, P.W.: Edge-directed interpolation. In: IEEE International Conference on Image Processing, vol. 3, pp. 707–710. IEEE (1996)

    Google Scholar 

  2. Argaw, D.M., Kim, J., Rameau, F., Kweon, I.S.: Motion-blurred video interpolation and extrapolation. In: AAAI Conference on Artificial Intelligence (2021)

    Google Scholar 

  3. Bao, W., Lai, W.S., Ma, C., Zhang, X., Gao, Z., Yang, M.H.: Depth-aware video frame interpolation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3703–3712 (2019)

    Google Scholar 

  4. Bao, W., Lai, W.S., Zhang, X., Gao, Z., Yang, M.H.: Memc-net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 43, 933–948 (2019)

    Google Scholar 

  5. Bengio, Y., Léonard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)

  6. Brooks, T., Barron, J.T.: Learning to synthesize motion blur. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6840–6848 (2019)

    Google Scholar 

  7. Caballero, J., et al.: Real-time video super-resolution with spatio-temporal networks and motion compensation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4778–4787 (2017)

    Google Scholar 

  8. Chan, K.C., Wang, X., Yu, K., Dong, C., Loy, C.C.: Understanding deformable alignment in video super-resolution. arXiv preprint arXiv:2009.07265 4(3), 4 (2020)

  9. Chan, K.C., Zhou, S., Xu, X., Loy, C.C.: Basicvsr++: improving video super-resolution with enhanced propagation and alignment. arXiv preprint arXiv:2104.13371 (2021)

  10. Cooper, M., Liu, T., Rieffel, E.: Video segmentation via temporal pattern classification. IEEE Trans. Multimedia 9(3), 610–618 (2007)

    Article  Google Scholar 

  11. Dachille, F., Kaufman, A.: High-degree temporal antialiasing. In: Proceedings Computer Animation, pp. 49–54. IEEE (2000)

    Google Scholar 

  12. Dai, J., et al.: Deformable convolutional networks. In: IEEE International Conference on Computer Vision, pp. 764–773 (2017)

    Google Scholar 

  13. Duchon, C.E.: Lanczos filtering in one and two dimensions. J. Appl. Meteorol. Climatol. 18(8), 1016–1022 (1979)

    Article  Google Scholar 

  14. Dutta, S., Shah, N.A., Mittal, A.: Efficient space-time video super resolution using low-resolution flow and mask upsampling. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 314–323 (2021)

    Google Scholar 

  15. Egan, K., Tseng, Y.T., Holzschuch, N., Durand, F., Ramamoorthi, R.: Frequency analysis and sheared reconstruction for rendering motion blur. ACM Trans. Graph. 28(3), 93–1 (2009)

    Google Scholar 

  16. Haris, M., Shakhnarovich, G., Ukita, N.: Space-time-aware multi-resolution video enhancement. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2859–2868 (2020)

    Google Scholar 

  17. Haris, M., Shakhnarovich, G., Ukita, N.: Recurrent back-projection network for video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3897–3906 (2019)

    Google Scholar 

  18. Holloway, J., Sankaranarayanan, A.C., Veeraraghavan, A., Tambe, S.: Flutter shutter video camera for compressive sensing of videos. In: International Conference on Computational Photography, pp. 1–9. IEEE (2012)

    Google Scholar 

  19. Huang, Y., Wang, W., Wang, L.: Video super-resolution via bidirectional recurrent convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 1015–1028 (2017)

    Article  Google Scholar 

  20. Isobe, T., Jia, X., Gu, S., Li, S., Wang, S., Tian, Q.: Video super-resolution with recurrent structure-detail network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 645–660. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_38

    Chapter  Google Scholar 

  21. Jiang, H., Sun, D., Jampani, V., Yang, M.H., Learned-Miller, E., Kautz, J.: Super slomo: high quality estimation of multiple intermediate frames for video interpolation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9000–9008 (2018)

    Google Scholar 

  22. Jin, M., Hu, Z., Favaro, P.: Learning to extract flawless slow motion from blurry videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8112–8121 (2019)

    Google Scholar 

  23. Jin, M., Meishvili, G., Favaro, P.: Learning to extract a video sequence from a single motion-blurred image. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6334–6342 (2018)

    Google Scholar 

  24. Jo, Y., Wug Oh, S., Kang, J., Joo Kim, S.: Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3224–3232 (2018)

    Google Scholar 

  25. Kalluri, T., Pathak, D., Chandraker, M., Tran, D.: Flavr: Flow-agnostic video representations for fast frame interpolation. arXiv preprint arXiv:2012.08512 (2020)

  26. Keelan, B.: Handbook of image quality: characterization and prediction. CRC Press (2002)

    Google Scholar 

  27. Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  28. Kim, H., Choi, M., Lim, B., Mu Lee, K.: Task-aware image downscaling. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 419–434. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_25

    Chapter  Google Scholar 

  29. Kim, S.Y., Oh, J., Kim, M.: Fisr: deep joint frame interpolation and super-resolution with a multi-scale temporal loss. In: AAAI Conference on Artificial Intelligence, pp. 11278–11286 (2020)

    Google Scholar 

  30. Kopf, J., Shamir, A., Peers, P.: Content-adaptive image downscaling. ACM Trans. Graph. 32(6), 1–8 (2013)

    Google Scholar 

  31. Korein, J., Badler, N.: Temporal anti-aliasing in computer generated animation. In: Annual Conference on Computer Graphics and Interactive Techniques. pp. 377–388 (1983)

    Google Scholar 

  32. Li, Y., Jin, P., Yang, F., Liu, C., Yang, M.H., Milanfar, P.: Comisr: Compression-informed video super-resolution. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2543–2552 (2021)

    Google Scholar 

  33. Liang, J., Cao, J., Fan, Y., Zhang, K., Ranjan, R., Li, Y., Timofte, R., Van Gool, L.: Vrt: A video restoration transformer. arXiv preprint arXiv:2201.12288 (2022)

  34. Liang, J., Fan, Y., Xiang, X., Ranjan, R., Ilg, E., Green, S., Cao, J., Zhang, K., Timofte, R., Van Gool, L.: Recurrent video restoration transformer with guided deformable attention. arXiv preprint arXiv:2206.02146 (2022)

  35. Lim, B., Lee, K.M.: Deep recurrent resnet for video super-resolution. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. pp. 1452–1455. IEEE (2017)

    Google Scholar 

  36. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 136–144 (2017)

    Google Scholar 

  37. Liu, C., Sun, D.: A bayesian approach to adaptive video super resolution. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 209–216. IEEE (2011)

    Google Scholar 

  38. Liu, Z., Yeh, R.A., Tang, X., Liu, Y., Agarwala, A.: Video frame synthesis using deep voxel flow. In: IEEE International Conference on Computer Vision, pp. 4463–4471 (2017)

    Google Scholar 

  39. Long, G., Kneip, L., Alvarez, J.M., Li, H., Zhang, X., Yu, Q.: Learning image matching by simply watching video. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 434–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_26

    Chapter  Google Scholar 

  40. Meyer, S., Wang, O., Zimmer, H., Grosse, M., Sorkine-Hornung, A.: Phase-based frame interpolation for video. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410–1418 (2015)

    Google Scholar 

  41. Mitchell, D.P., Netravali, A.N.: Reconstruction filters in computer-graphics. ACM Siggraph Comput. Graph. 22(4), 221–228 (1988)

    Article  Google Scholar 

  42. Mudenagudi, U., Banerjee, S., Kalra, P.K.: Space-time super-resolution using graph-cut optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 995–1008 (2010)

    Article  Google Scholar 

  43. Nah, S., Baik, S., Hong, S., Moon, G., Son, S., Timofte, R., Lee, K.M.: Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 2019

    Google Scholar 

  44. Niklaus, S., Liu, F.: Context-aware synthesis for video frame interpolation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1710 (2018)

    Google Scholar 

  45. Niklaus, S., Mai, L., Liu, F.: Video frame interpolation via adaptive convolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 670–679 (2017)

    Google Scholar 

  46. Niklaus, S., Mai, L., Liu, F.: Video frame interpolation via adaptive separable convolution. In: IEEE International Conference on Computer Vision, pp. 261–270 (2017)

    Google Scholar 

  47. Niyogi, S.A., Adelson, E.H.: Analyzing gait with spatiotemporal surfaces. In: IEEE Workshop on Motion of Non-rigid and Articulated Objects, pp. 64–69. IEEE (1994)

    Google Scholar 

  48. Oeztireli, A.C., Gross, M.: Perceptually based downscaling of images. ACM Trans. Graph. 34(4), 1–10 (2015)

    Article  Google Scholar 

  49. Zuckerman, L.P., Naor, E., Pisha, G., Bagon, S., Irani, M.: Across scales and across dimensions: temporal super-resolution using deep internal learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 52–68. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58571-6_4

    Chapter  Google Scholar 

  50. Purohit, K., Shah, A., Rajagopalan, A.: Bringing alive blurred moments. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6830–6839 (2019)

    Google Scholar 

  51. Raskar, R., Agrawal, A., Tumblin, J.: Coded exposure photography: Motion deblurring using fluttered shutter. ACM Trans. Graph. 25(3), 795–804 (2006)

    Article  Google Scholar 

  52. Ray, S.: Scientific photography and applied imaging. Routledge (1999)

    Google Scholar 

  53. Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K.: High dynamic range imaging: acquisition, display, and image-based lighting. Morgan Kaufmann (2010)

    Google Scholar 

  54. Rengarajan, V., Zhao, S., Zhen, R., Glotzbach, J., Sheikh, H., Sankaranarayanan, A.C.: Photosequencing of motion blur using short and long exposures. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 510–511 (2020)

    Google Scholar 

  55. Research, M.: fvcore (2019). https://github.com/facebookresearch/fvcore

  56. Rim, J., Kim, G., Kim, J., Lee, J., Lee, S., Cho, S.: Realistic blur synthesis for learning image deblurring. arXiv preprint arXiv:2202.08771 (2022)

  57. Rogozhnikov, A.: Einops: clear and reliable tensor manipulations with einstein-like notation. In: International Conference on Learning Representations (2021)

    Google Scholar 

  58. Sajjadi, M.S., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6626–6634 (2018)

    Google Scholar 

  59. Shahar, O., Faktor, A., Irani, M.: Space-time super-resolution from a single video. IEEE (2011)

    Google Scholar 

  60. Shannon, C.: Communication in the presence of noise. Proc. IRE 37(1), 10–21 (1949). https://doi.org/10.1109/jrproc.1949.232969

    Article  MathSciNet  Google Scholar 

  61. Shechtman, E., Caspi, Y., Irani, M.: Increasing space-time resolution in video. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 753–768. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47969-4_50

    Chapter  Google Scholar 

  62. Shen, W., Bao, W., Zhai, G., Chen, L., Min, X., Gao, Z.: Blurry video frame interpolation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5114–5123 (2020)

    Google Scholar 

  63. Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)

    Google Scholar 

  64. Shinya, M.: Spatial anti-aliasing for animation sequences with spatio-temporal filtering. In: Annual Conference on Computer Graphics and Interactive Techniques, pp. 289–296 (1993)

    Google Scholar 

  65. Sim, H., Oh, J., Kim, M.: Xvfi: extreme video frame interpolation. In: IEEE International Conference on Computer Vision (2021)

    Google Scholar 

  66. Sun, W., Chen, Z.: Learned image downscaling for upscaling using content adaptive resampler. IEEE Trans. Image Process. 29, 4027–4040 (2020)

    Article  MATH  Google Scholar 

  67. Suzuki, T.: Optical low-pass filter (Jan 1987)

    Google Scholar 

  68. Takeda, H., Van Beek, P., Milanfar, P.: Spatiotemporal video upscaling using motion-assisted steering kernel (mask) regression. In: Mrak, M., Grgic, M., Kunt, M. (eds.) High-Quality Visual Experience, pp. 245–274. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12802-8_10

    Chapter  Google Scholar 

  69. Talebi, H., Milanfar, P.: Learning to resize images for computer vision tasks. In: IEEE International Conference on Computer Vision, pp. 497–506, October 2021

    Google Scholar 

  70. Tao, X., Gao, H., Liao, R., Wang, J., Jia, J.: Detail-revealing deep video super-resolution. In: IEEE International Conference on Computer Vision, pp. 4472–4480 (2017)

    Google Scholar 

  71. Tian, Y., Zhang, Y., Fu, Y., Xu, C.: Tdan: Temporally-deformable alignment network for video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3360–3369 (2020)

    Google Scholar 

  72. Tomar, S.: Converting video formats with ffmpeg. Linux J. 2006(146), 10 (2006)

    Google Scholar 

  73. Trentacoste, M., Mantiuk, R., Heidrich, W.: Blur-aware image downsampling. In: Computer Graphics Forum, vol. 30, pp. 573–582. Wiley Online Library (2011)

    Google Scholar 

  74. Triggs, B.: Empirical filter estimation for subpixel interpolation and matching. In: IEEE International Conference on Computer Visio, vol. 2, pp. 550–557. IEEE (2001)

    Google Scholar 

  75. Wang, H., Xiang, X., Tian, Y., Yang, W., Liao, Q.: Stdan: deformable attention network for space-time video super-resolution. arXiv preprint arXiv:2203.06841 (2022)

  76. Wang, L., Guo, Y., Lin, Z., Deng, X., An, W.: Learning for video super-resolution through hr optical flow estimation. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11361, pp. 514–529. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20887-5_32

    Chapter  Google Scholar 

  77. Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: video restoration with enhanced deformable convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  78. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  79. Weber, N., Waechter, M., Amend, S.C., Guthe, S., Goesele, M.: Rapid, detail-preserving image downscaling. ACM Trans. Graph. 35(6), 1–6 (2016)

    Article  Google Scholar 

  80. Wei, Y., Chen, L., Song, L.: Video compression based on jointly learned down-sampling and super-resolution networks. In: 2021 International Conference on Visual Communications and Image Processing (VCIP), pp. 1–5. IEEE (2021)

    Google Scholar 

  81. Wills, J., Agarwal, S., Belongie, S.: What went where [motion segmentation]. In: IEEE Conference on Computer Vision and Pattern Recognition (2003)

    Google Scholar 

  82. Xiang, X., Lin, Q., Allebach, J.P.: Boosting high-level vision with joint compression artifacts reduction and super-resolution. In: International Conference on Pattern Recognition. IEEE (2020)

    Google Scholar 

  83. Xiang, X., Tian, Y., Zhang, Y., Fu, Y., Allebach, J.P., Xu, C.: Zooming slow-mo: fast and accurate one-stage space-time video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3370–3379 (2020)

    Google Scholar 

  84. Xiang, X., Tian, Y., Zhang, Y., Fu, Y., Allebach, J.P., Xu, C.: Zooming slowmo: an efficient one-stage framework for space-time video super-resolution. arXiv preprint arXiv:2104.07473 (2021)

  85. Xiao, Z., Xiong, Z., Fu, X., Liu, D., Zha, Z.J.: Space-time video super-resolution using temporal profiles. In: ACM International Conference on Multimedia, pp. 664–672 (2020)

    Google Scholar 

  86. Xu, G., Xu, J., Li, Z., Wang, L., Sun, X., Cheng, M.M.: Temporal modulation network for controllable space-time video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6388–6397 (2021)

    Google Scholar 

  87. Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. Int. J. Comput. Vision 127(8), 1106–1125 (2019)

    Article  Google Scholar 

  88. Yuan, L., Sun, J., Quan, L., Shum, H.Y.: Image deblurring with blurred/noisy image pairs. ACM Trans. Graph. 26(3) (2007)

    Google Scholar 

  89. Zelnik-Manor, L., Irani, M.: Event-based analysis of video. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-II. IEEE (2001)

    Google Scholar 

  90. Zhang, K., Luo, W., Stenger, B., Ren, W., Ma, L., Li, H.: Every moment matters: Detail-aware networks to bring a blurry image alive. In: ACM International Conference on Multimedia, pp. 384–392 (2020)

    Google Scholar 

  91. Zhang, R.: Making convolutional networks shift-invariant again. In: International Conference on Machine Learning, pp. 7324–7334. PMLR (2019)

    Google Scholar 

  92. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472–2481 (2018)

    Google Scholar 

  93. Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets v2: more deformable, better results. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9308–9316 (2019)

    Google Scholar 

  94. Zou, X., Xiao, F., Yu, Z., Lee, Y.: Delving deeper into anti-aliasing in convnets. In: British Machine Vision Conference (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Meta Reality Labs, Menlo Park, USA

    Xiaoyu Xiang, Vijay Rengarajan, Lucas D. Young, Bo Zhu & Rakesh Ranjan

  2. University of Texas at Dallas, Richardson, USA

    Yapeng Tian

Authors
  1. Xiaoyu Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yapeng Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vijay Rengarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lucas D. Young
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bo Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rakesh Ranjan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoyu Xiang .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 3817 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xiang, X., Tian, Y., Rengarajan, V., Young, L.D., Zhu, B., Ranjan, R. (2022). Learning Spatio-Temporal Downsampling for Effective Video Upscaling. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13678. Springer, Cham. https://doi.org/10.1007/978-3-031-19797-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19797-0_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19796-3

  • Online ISBN: 978-3-031-19797-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation