RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression

RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/rawic-bit-depth-adaptive-lossless-raw-image-compression

stale

Proof freshness: stale
Proof status: partial
Display score: 7/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 44
Source count: 4
Coverage: 83%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID rawic-bit-depth-adaptive-lossless-raw-image-compression | Route /signal-canvas/rawic-bit-depth-adaptive-lossless-raw-image-compression

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/rawic-bit-depth-adaptive-lossless-raw-image-compression

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: 44

Proof: Verification pending

Freshness state: computing

Source paper: RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression

PDF: https://arxiv.org/pdf/2603.28105v1

Repository: https://github.com/chunbaobao/RAWIC

Source count: 4

Coverage: 83%

Last proof check: 2026-03-31T20:30:20.275Z

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Ready for execution: RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression

/buildability/rawic-bit-depth-adaptive-lossless-raw-image-compression

Build Nowready

Subject: RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression

Verdict

Build Now

Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.

GitHub Code Pulse

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Health

Last commit

4/2/2026

Forks

Open repository

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Experiments show that RAWIC consistently surpasses traditional lossless codecs, achieving an average 7.7% bitrate reduction over JPEG-XL.
Implicationpartial
Directly stated in the abstract with supporting numeric results in the performance table.
Verificationpartial
partial
Evidencepartial
To address these challenges, we introduce RAWIC, a bit-depth-adaptive learned lossless compression framework for Bayer-pattern raw images.
Implicationpartial
Explicitly stated in the abstract as the core contribution of the paper.
Verificationpartial
partial
Evidencepartial
Existing learned lossless image compression methods predominantly focus on sRGB images with a fixed 8-bit depth per channel.
Implicationpartial
Directly stated in the methodology section as the motivation for the work.
Verificationpartial
partial
Evidencepartial
This architecture enables a single model to handle raw images from diverse cameras and bit depths.
Implicationpartial
Explicitly stated in the abstract as a key feature of the proposed method.
Verificationpartial
partial
Evidencepartial
We first convert single-channel Bayer data into a four-channel RGGB format and partition it into patches.
Implicationpartial
Directly described in the abstract as a core step in the method.
Verificationpartial
partial
Evidencepartial
RAWIC (Ours)6.79 7.47 5.11 5.99 5.83 7.80
Implicationpartial
Strongly supported by the performance table showing lower BPP (bits per pixel) for RAWIC compared to all listed codecs.
Verificationpartial
partial
Evidencepartial
A bit-depth-adaptive entropy model is then designed to estimate patch distributions conditioned on their bit depths.
Implicationpartial
Directly stated in the abstract as a key component of the framework.
Verificationpartial
partial
Evidencepartial
NOTE THAT OUR METHOD INCLUDES THE BITRATES FOR STORING BIT DEPTHS.
Implicationpartial
Explicitly noted in the performance table caption, indicating a comprehensive evaluation.
Verificationpartial
partial

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RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression

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