Strümpler et al.'s 2022 ECCV follow-up to COIN also omits fractal, IFS, and WFA compression from its related work and credits COIN as the first INR-based image-compression approach

implicit-neural-representationsimage-compressioncoinfractal-compressionnegative-citationargument-from-silencehistory-of-machine-learning

Strümpler, Postels, Yang, Van Gool & Tombari's "Implicit Neural Representations for Image Compression" (ECCV 2022) — the most-cited direct successor to COIN — traces INR-based image compression's origin entirely within the neural-network tradition: DeepSDF → occupancy networks → NeRF → COIN. Its related-work section states plainly: "Dupont et al. [19] propose the first INR-based image compression approach COIN, which overfits an INR's model weights to represent single [images]." No mention of fractal, IFS, PIFS, or weighted-finite-automata (WFA) compression appears anywhere in the paper.

The "first" framing is the notable part: fractal image compression is, on its own historical terms, an earlier "encode-the-image-as-a-compact-function-not-a-grid" approach (claim-fractal-compression-to-implicit-neural-representations-bridge), reaching commercial products in the 1990s. Strümpler et al. are not wrong that COIN was the first INR-based approach — but the paper's framing treats the whole idea of function-valued image compression as originating with COIN, without engaging the older tradition that made a structurally similar claim first.

This is the third leg of the vault's three-paper negative-citation check, alongside claim-siren-2020-does-not-cite-fractal-ifs-wfa-compression and claim-coin-2021-does-not-cite-fractal-ifs-wfa-compression. All three canonical INR/INR-compression papers omit the fractal lineage; contrast claim-poli-et-al-2022-neural-collages-bridges-fractal-and-inr-canons, published only months earlier, which draws the connection explicitly from the fractal-compression side. See also moc-argument-from-silence.