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Quantum Implicit Neural Compression

arXiv 2412.19828 ↗First posted 19 Dec 2024eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Signal compression based on implicit neural representation (INR) is an emerging technique to represent multimedia signals with a small number of bits. While INR-based signal compression achieves high-quality reconstruction for relatively low-resolution signals, the accuracy of high-frequency details is significantly degraded with a small model. To improve the compression efficiency of INR, we introduce quantum INR (quINR), which leverages the exponentially rich expressivity of quantum neural networks for data compression. Evaluations using some benchmark datasets show that the proposed quINR-based compression could improve rate-distortion performance in image compression compared with traditional codecs and classic INR-based coding methods, up to 1.2dB gain.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 19 Dec 2024.

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References · 19

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  2. 2021NeRV: Neural Representations for Videoscited by 256
  3. 2021COIN: COmpression with Implicit Neural representationscited by 233
  4. 2018Quantum generative adversarial networkscited by 128
  5. 2022COIN++: Neural Compression Across Modalitiescited by 82
  6. 2023HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representationcited by 71
  7. 2023C3: High-performance and low-complexity neural compression from a single image or videocited by 55
  8. 2022COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codeccited by 51
  9. 2022NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modelingcited by 51
  10. 2023Generative quantum machine learning via denoising diffusion probabilistic modelscited by 30
  11. 2024Boosting Neural Representations for Videos with a Conditional Decodercited by 29
  12. 2024NVRC: Neural Video Representation Compressioncited by 29
7 more
  1. 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptationcited by 23
  2. 2023Quantum-Noise-Driven Generative Diffusion Modelscited by 20
  3. 2021Quantum Graph Convolutional Neural Networkscited by 18
  4. 2024Quantum Implicit Neural Representationscited by 16
  5. 2023Low-complexity Overfitted Neural Image Codeccited by 13
  6. 2024Immersive Video Compression using Implicit Neural Representationscited by 12
  7. 2024Quantum Multimodal Contrastive Learning Frameworkcited by 10

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