# Towards a quality metric for dense light fields

arXiv: https://arxiv.org/abs/1704.07576 · HTML: https://mlsanity.com/paper/1704.07576/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2017.

## Built on by (most cited first, 15 of 15)

- [Mixed Neural Voxels for Fast Multi-view Video Synthesis](https://mlsanity.com/paper/2212.00190/) (2022)
- [VRSplat: Fast and Robust Gaussian Splatting for Virtual Reality](https://mlsanity.com/paper/2505.10144/) (2025)
- [Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network](https://mlsanity.com/paper/2104.06797/) (2021)
- [Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines](https://mlsanity.com/paper/2108.07408/) (2021)
- [Content-aware Warping for View Synthesis](https://mlsanity.com/paper/2201.09023/) (2022)
- [Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field Images](https://mlsanity.com/paper/2311.01292/) (2023)
- [Puzzle Similarity: A Perceptually-guided Cross-Reference Metric for Artifact Detection in 3D Scene Reconstructions](https://mlsanity.com/paper/2411.17489/) (2024)
- [LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Space](https://mlsanity.com/paper/2303.10961/) (2023)
- [Learning GAN-based Foveated Reconstruction to Recover Perceptually Important Image Features](https://mlsanity.com/paper/2108.03499/) (2021)
- [Geo-NI: Geometry-aware Neural Interpolation for Light Field Rendering](https://mlsanity.com/paper/2206.09736/) (2022)
- [Single-Shot Metric Depth from Focused Plenoptic Cameras](https://mlsanity.com/paper/2412.02386/) (2024)
- [Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration](https://mlsanity.com/paper/2311.15846/) (2023)
- [Beyond MOS: Subjective Image Quality Score Preprocessing Method Based on Perceptual Similarity](https://mlsanity.com/paper/2404.19666/) (2024)
- [Light-Field Dataset for Disparity Based Depth Estimation](https://mlsanity.com/paper/2511.05866/) (2025)
- [Towards Standardized Light Field Quality Assessment: Hybrid Subjective Benchmarking and Objective Metric Evaluation](https://mlsanity.com/paper/2607.03494/) (2026)

## Abstract (arXiv)

Light fields become a popular representation of three dimensional scenes, and there is interest in their processing, resampling, and compression. As those operations often result in loss of quality, there is a need to quantify it. In this work, we collect a new dataset of dense reference and distorted light fields as well as the corresponding quality scores which are scaled in perceptual units. The scores were acquired in a subjective experiment using an interactive light-field viewing setup. The dataset contains typical artifacts that occur in light-field processing chain due to light-field reconstruction, multi-view compression, and limitations of automultiscopic displays. We test a number of existing objective quality metrics to determine how well they can predict the quality of light fields. We find that the existing image quality metrics provide good measures of light-field quality, but require dense reference light- fields for optimal performance. For more complex tasks of comparing two distorted light fields, their performance drops significantly, which reveals the need for new, light-field-specific metrics.