# SADN: Learned Light Field Image Compression with Spatial-Angular Decorrelation

arXiv: https://arxiv.org/abs/2202.10837 · HTML: https://mlsanity.com/paper/2202.10837/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Spatial-Angular Interaction for Light Field Image Super-Resolution](https://mlsanity.com/paper/1912.07849/) (2019)
- [LiFF: Light Field Features in Scale and Depth](https://mlsanity.com/paper/1901.03916/) (2019)

## Built on by (most cited first, 6 of 6)

- [QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression](https://mlsanity.com/paper/2303.05744/) (2023)
- [Learned Focused Plenoptic Image Compression with Microimage Preprocessing and Global Attention](https://mlsanity.com/paper/2305.00489/) (2023)
- [MiNL: Micro-images based Neural Representation for Light Fields](https://mlsanity.com/paper/2209.08277/) (2022)
- [Light Field Compression Based on Implicit Neural Representation](https://mlsanity.com/paper/2407.10325/) (2024)
- [LFIC-DRASC: Deep Light Field Image Compression Using Disentangled Representation and Asymmetrical Strip Convolution](https://mlsanity.com/paper/2409.11711/) (2024)
- [SANR: Scene-Aware Neural Representation for Light Field Image Compression with Rate-Distortion Optimization](https://mlsanity.com/paper/2510.15775/) (2025)

## Abstract (arXiv)

Light field image becomes one of the most promising media types for immersive video applications. In this paper, we propose a novel end-to-end spatial-angular-decorrelated network (SADN) for high-efficiency light field image compression. Different from the existing methods that exploit either spatial or angular consistency in the light field image, SADN decouples the angular and spatial information by dilation convolution and stride convolution in spatial-angular interaction, and performs feature fusion to compress spatial and angular information jointly. To train a stable and robust algorithm, a large-scale dataset consisting of 7549 light field images is proposed and built. The proposed method provides 2.137 times and 2.849 times higher compression efficiency relative to H.266/VVC and H.265/HEVC inter coding, respectively. It also outperforms the end-to-end image compression networks by an average of 79.6% bitrate saving with much higher subjective quality and light field consistency.