mlsanityData to 14 Oct 2026

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

arXiv 2206.05279 ↗First posted 10 Jun 2022eess.IV cs.CV cs.ITcited by 5

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Abstract (arXiv)

Generative model based image lossless compression algorithms have seen a great success in improving compression ratio. However, the throughput for most of them is less than 1 MB/s even with the most advanced AI accelerated chips, preventing them from most real-world applications, which often require 100 MB/s. In this paper, we propose PILC, an end-to-end image lossless compression framework that achieves 200 MB/s for both compression and decompression with a single NVIDIA Tesla V100 GPU, 10 times faster than the most efficient one before. To obtain this result, we first develop an AI codec that combines auto-regressive model and VQ-VAE which performs well in lightweight setting, then we design a low complexity entropy coder that works well with our codec. Experiments show that our framework compresses better than PNG by a margin of 30% in multiple datasets. We believe this is an important step to bring AI compression forward to commercial use.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 29 of its 51 references that are in the map. First posted on arXiv 10 Jun 2022.

Builds on

29 of 51 references.

References · 29

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17 more
  1. 2019Variable Rate Deep Image Compression With a Conditional Autoencodercited by 114
  2. 2018Practical Full Resolution Learned Lossless Image Compressioncited by 94
  3. 2019Practical Lossless Compression with Latent Variables using Bits Back Codingcited by 86
  4. 2019Integer Discrete Flows and Lossless Compressioncited by 83
  5. 2020Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Featurescited by 64
  6. 2019Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variablescited by 52
  7. 2019HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Modelscited by 37
  8. 2020IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compressioncited by 36
  9. 2019Compression with Flows via Local Bits-Back Codingcited by 34
  10. 2019Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compressioncited by 33
  11. 2021On the Out-of-distribution Generalization of Probabilistic Image Modellingcited by 33
  12. 2019Learning Content-Weighted Deep Image Compressioncited by 28
  13. 2021iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compressioncited by 27
  14. 2021iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Codercited by 26
  15. 2019Deep Perceptual Compressioncited by 9
  16. 2019Human Perceptual Evaluations for Image Compressioncited by 5
  17. 2020Saliency Driven Perceptual Image Compressioncited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2025Joint Lossless Compression and Steganography for Medical Images via Large Language Modelscited by 6
  2. 2023Effortless Cross-Platform Video Codec: A Codebook-Based Methodcited by 1
  3. 2026Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmissioncited by 1
  4. 2023Streaming Lossless Volumetric Compression of Medical Images Using Gated Recurrent Convolutional Neural Networkcited by 0
  5. 2024FLLIC: Functionally Lossless Image Compressioncited by 0