mlsanityData to 14 Oct 2026

GABIC: Graph-based Attention Block for Image Compression

arXiv 2410.02981 ↗First posted 3 Oct 2024eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

While standardized codecs like JPEG and HEVC-intra represent the industry standard in image compression, neural Learned Image Compression (LIC) codecs represent a promising alternative. In detail, integrating attention mechanisms from Vision Transformers into LIC models has shown improved compression efficiency. However, extra efficiency often comes at the cost of aggregating redundant features. This work proposes a Graph-based Attention Block for Image Compression (GABIC), a method to reduce feature redundancy based on a k-Nearest Neighbors enhanced attention mechanism. Our experiments show that GABIC outperforms comparable methods, particularly at high bit rates, enhancing compression performance.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 3 Oct 2024.

Builds on

21 of 30 references.

References · 21

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9 more
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Built on by

Cited by year (2026 to 14 Oct)

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

4 papers.

  1. 2025Content-Aware Mamba for Learned Image Compressioncited by 6
  2. 2024Efficient Progressive Image Compression with Variance-aware Maskingcited by 4
  3. 2026Adaptive Learned Image Compression with Graph Neural Networkscited by 1
  4. 2025Spectral and Spatial Graph Learning for Multispectral Solar Image Compressioncited by 0