Raw-JPEG Adapter: Efficient Raw Image Compression with JPEG
This addresses storage constraints for raw images in digital cameras, though it is incremental as it builds on existing JPEG infrastructure.
The paper tackles the problem of storing raw camera images efficiently by developing RawJPEG Adapter, a learnable preprocessing pipeline that adapts raw images for standard JPEG compression, achieving higher fidelity than direct JPEG storage with a favorable trade-off between compression ratio and reconstruction accuracy.
Digital cameras digitize scene light into linear raw representations, which the image signal processor (ISP) converts into display-ready outputs. While raw data preserves full sensor information--valuable for editing and vision tasks--formats such as Digital Negative (DNG) require large storage, making them impractical in constrained scenarios. In contrast, JPEG is a widely supported format, offering high compression efficiency and broad compatibility, but it is not well-suited for raw storage. This paper presents RawJPEG Adapter, a lightweight, learnable, and invertible preprocessing pipeline that adapts raw images for standard JPEG compression. Our method applies spatial and optional frequency-domain transforms, with compact parameters stored in the JPEG comment field, enabling accurate raw reconstruction. Experiments across multiple datasets show that our method achieves higher fidelity than direct JPEG storage, supports other codecs, and provides a favorable trade-off between compression ratio and reconstruction accuracy.