# Raw Image Reconstruction with Learned Compact Metadata

## TL;DR

This paper introduces a novel end-to-end framework for raw image compression that learns a compact latent representation as metadata, improving reconstruction quality while reducing metadata size and computational speed.

## Contribution

It proposes a new latent space representation for raw image compression and a novel sRGB-guided context model with enhanced entropy estimation strategies.

## Key findings

- Achieves superior raw image reconstruction with smaller metadata size.
- Outperforms existing methods on uncompressed sRGB and JPEG images.
- Provides adaptive bit allocation for important image regions.

## Abstract

While raw images exhibit advantages over sRGB images (e.g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements. Very recent works propose to compress raw images by designing the sampling masks in the raw image pixel space, leading to suboptimal image representations and redundant metadata. In this paper, we propose a novel framework to learn a compact representation in the latent space serving as the metadata in an end-to-end manner. Furthermore, we propose a novel sRGB-guided context model with improved entropy estimation strategies, which leads to better reconstruction quality, smaller size of metadata, and faster speed. We illustrate how the proposed raw image compression scheme can adaptively allocate more bits to image regions that are important from a global perspective. The experimental results show that the proposed method can achieve superior raw image reconstruction results using a smaller size of the metadata on both uncompressed sRGB images and JPEG images.