mlsanityData to 14 Oct 2026

Block-optimized Variable Bit Rate Neural Image Compression

arXiv 1805.10887 ↗First posted 28 May 2018cs.LG stat.MLcited by 7

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Abstract (arXiv)

In this work, we propose an end-to-end block-based auto-encoder system for image compression. We introduce novel contributions to neural-network based image compression, mainly in achieving binarization simulation, variable bit rates with multiple networks, entropy-friendly representations, inference-stage code optimization and performance-improving normalization layers in the auto-encoder. We evaluate and show the incremental performance increase of each of our contributions.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 May 2018.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 2.

7 papers.

  1. 2021Overfitting for Fun and Profit: Instance-Adaptive Data Compressioncited by 38
  2. 2022Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compressioncited by 7
  3. 2021Adaptation and Attention for Neural Video Codingcited by 6
  4. 2021Substitutional Neural Image Compressioncited by 3
  5. 2022Improving The Reconstruction Quality by Overfitted Decoder Bias in Neural Image Compressioncited by 2
  6. 2024Robustly overfitting latents for flexible neural image compressioncited by 2
  7. 2024Exploration of Learned Lifting-Based Transform Structures for Fully Scalable and Accessible Wavelet-Like Image Compressioncited by 2