# View Sub-sampling and Reconstruction for Efficient Light Field Compression

arXiv: https://arxiv.org/abs/2208.06464 · HTML: https://mlsanity.com/paper/2208.06464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Light Field Compression with Disparity Guided Sparse Coding based on Structural Key Views](https://mlsanity.com/paper/1610.03684/) (2016)
- [Light Field Image Coding Using Dual Discriminator Generative Adversarial Network and VVC Temporal Scalability](https://mlsanity.com/paper/2003.12322/) (2020)
- [Learning-Based Practical Light Field Image Compression Using A Disparity-Aware Model](https://mlsanity.com/paper/2106.11558/) (2021)
- [A Pipeline for Lenslet Light Field Quality Enhancement](https://mlsanity.com/paper/1808.05387/) (2018)
- [A Study of Efficient Light Field Subsampling and Reconstruction Strategies](https://mlsanity.com/paper/2008.04694/) (2020)
- [Self-supervised Light Field View Synthesis Using Cycle Consistency](https://mlsanity.com/paper/2008.05084/) (2020)

## Abstract (arXiv)

Compression is an important task for many practical applications of light fields. Although previous work has proposed numerous methods for efficient light field compression, the effect of view selection on this task is not well exploited. In this work, we study different sub-sampling and reconstruction strategies for light field compression. We apply various sub-sampling and corresponding reconstruction strategies before and after light field compression. Then, fully reconstructed light fields are assessed to evaluate the performance of different methods. Our evaluation is performed on both real-world and synthetic datasets, and optimal strategies are devised from our experimental results. We hope this study would be beneficial for future research such as light field streaming, storage, and transmission.