mlsanityData to 7 Oct 2026

Embedding Novel Views in a Single JPEG Image

arXiv 2108.13003 ↗First posted 30 Aug 2021cs.CVcited by 10

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Abstract (arXiv)

We propose a novel approach for embedding novel views in a single JPEG image while preserving the perceptual fidelity of the modified JPEG image and the restored novel views. We adopt the popular novel view synthesis representation of multiplane images (MPIs). Our model first encodes 32 MPI layers (totally 128 channels) into a 3-channel JPEG image that can be decoded for MPIs to render novel views, with an embedding capacity of 1024 bits per pixel. We conducted experiments on public datasets with different novel view synthesis methods, and the results show that the proposed method can restore high-fidelity novel views from a slightly modified JPEG image. Furthermore, our method is robust to JPEG compression, color adjusting, and cropping. Our source code will be publicly available.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 30 Aug 2021.

Builds on

18 of 33 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  6. 2018HiDDeN: Hiding Data With Deep Networkscited by 384
  7. 2019SynSin: End-to-end View Synthesis from a Single Imagecited by 364
  8. 2020Single-View View Synthesis with Multiplane Imagescited by 263
  9. 2019StegaStamp: Invisible Hyperlinks in Physical Photographscited by 256
  10. 2019DeepView: View Synthesis with Learned Gradient Descentcited by 253
  11. 2017Photographic Image Synthesis with Cascaded Refinement Networkscited by 223
  12. 20203D Photography using Context-aware Layered Depth Inpaintingcited by 206
6 more
  1. 2019Pushing the Boundaries of View Extrapolation with Multiplane Imagescited by 191
  2. 2019Web Stereo Video Supervision for Depth Prediction from Dynamic Scenescited by 73
  3. 2017Generating Steganographic Images via Adversarial Trainingcited by 57
  4. 2020Semantic View Synthesiscited by 18
  5. 2020Generative View Synthesis: From Single-view Semantics to Novel-view Imagescited by 12
  6. 2020Mononizing Binocular Videoscited by 9

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Citing papers per year: 2022: 2; 2023: 4; 2024: 3; 2025: 1.

10 papers.