mlsanityData to 7 Oct 2026

RQAT-INR: Improved Implicit Neural Image Compression

arXiv 2303.03028 ↗First posted 6 Mar 2023cs.CVcited by 9

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Abstract (arXiv)

Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compression rates compared to the decades long traditional codecs such as AVC, HEVC or VVC. However, because of complexity and energy consumption, these approaches are still far away from practical usage in industry. More recently, implicit neural representation (INR) based codecs have emerged, and have lower complexity and energy usage to classical approaches at decoding. However, their performances are not in par at the moment with state-of-the-art methods. In this research, we first show that INR based image codec has a lower complexity than VAE based approaches, then we propose several improvements for INR-based image codec and outperformed baseline model by a large margin.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 6 Mar 2023.

Builds on

14 of 22 references.

References · 14

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  2. 2018Variational image compression with a scale hyperpriorcited by 910
  3. 2016End-to-end Optimized Image Compressioncited by 673
  4. 2020Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modulescited by 570
  5. 2021A White Paper on Neural Network Quantizationcited by 453
  6. 2020CompressAI: a PyTorch library and evaluation platform for end-to-end compression researchcited by 271
  7. 2021NeRV: Neural Representations for Videoscited by 255
  8. 2021COIN: COmpression with Implicit Neural representationscited by 232
  9. 2021Implicit Neural Representations for Image Compressioncited by 161
  10. 2021Enhanced Invertible Encoding for Learned Image Compressioncited by 102
  11. 2022Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compressioncited by 7
  12. 2022Video Coding Using Learned Latent GAN Compressioncited by 4
2 more
  1. 2022Reducing The Mismatch Between Marginal and Learned Distributions in Neural Video Compressioncited by 2
  2. 2021FacialFilmroll: High-resolution multi-shot video editingcited by 1

Built on by

Cited by year

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

9 papers.

  1. 2023C3: High-performance and low-complexity neural compression from a single image or videocited by 54
  2. 2023Improved Positional Encoding for Implicit Neural Representation based Compact Data Representationcited by 4
  3. 2025UAR-NVC: A Unified AutoRegressive Framework for Memory-Efficient Neural Video Compressioncited by 3
  4. 2024SLIC: A Learned Image Codec Using Structure and Colorcited by 2
  5. 2024Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimizationcited by 2
  6. 20233D Compression Using Neural Fieldscited by 1
  7. 2023A Compact Implicit Neural Representation for Efficient Storage of Massive 4D Functional Magnetic Resonance Imagingcited by 1
  8. 2024D'OH: Decoder-Only Random Hypernetworks for Implicit Neural Representationscited by 0
  9. 2025From Noise to Latent: Generating Gaussian Latents for INR-Based Image Compressioncited by 0