# Towards Standardized Light Field Quality Assessment: Hybrid Subjective Benchmarking and Objective Metric Evaluation

arXiv: https://arxiv.org/abs/2607.03494 · HTML: https://mlsanity.com/paper/2607.03494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 61 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Builds on (most cited first, 19 of 19)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2011.06294/) (2020)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence](https://mlsanity.com/paper/1505.01462/) (2015)
- [Shift-tolerant Perceptual Similarity Metric](https://mlsanity.com/paper/2207.13686/) (2022)
- [Perceptual Video Quality Assessment: A Survey](https://mlsanity.com/paper/2402.03413/) (2024)
- [ColorVideoVDP: A visual difference predictor for image, video and display distortions](https://mlsanity.com/paper/2401.11485/) (2024)
- [Revisiting Adaptive Convolutions for Video Frame Interpolation](https://mlsanity.com/paper/2011.01280/) (2020)
- [A practical guide and software for analysing pairwise comparison experiments](https://mlsanity.com/paper/1712.03686/) (2017)
- [Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization](https://mlsanity.com/paper/2004.05691/) (2020)
- [Perceptually Optimized Image Rendering](https://mlsanity.com/paper/1701.06641/) (2017)
- [Towards a quality metric for dense light fields](https://mlsanity.com/paper/1704.07576/) (2017)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator](https://mlsanity.com/paper/2211.04927/) (2022)
- [Subjective Image Quality Assessment with Boosted Triplet Comparisons](https://mlsanity.com/paper/2108.00201/) (2021)
- [Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy](https://mlsanity.com/paper/2010.00370/) (2020)
- [Subjective Evaluation of Low Distortion Coded Light Fields with View Synthesis](https://mlsanity.com/paper/2509.14761/) (2025)

## Abstract (arXiv)

Benchmarking immersive media coding solutions, especially in the standardization context, requires reliable and reproducible subjective quality assessment (QA) procedures, along with objective quality metrics that remain accurate across different distortion types. This paper presents a standardized workflow for light field QA, developed and deployed in the context of JPEG Pleno standardization activities, which integrates benchmark generation, a hybrid subjective evaluation, and objective metric analysis into a common workflow. The benchmark is designed to encompass not only traditional coding-only artifacts but also distortions that arise in processing pipelines in which light field encoding is accompanied with view synthesis and reconstruction techniques. A hybrid subjective method is proposed enabling fine-grained assessment by combining reference-anchored quality rating with targeted pairwise refinement in perceptually ambiguous regions. The reliability of subjective scores is verified using statistical consistency analyses between observers of two cohorts. Finally, a large set of objective metrics is systematically evaluated in terms of global prediction accuracy, local agreement in ambiguous quality regions, and robustness across distortion families. The results show that several metrics achieve strong agreement for coding-only stimuli, but their performance consistently drops when view synthesis distortions are included. The analysis further highlights the importance of view-pooling strategy in the design of future light field quality metrics. The work provides a reproducible and standardization-ready framework for fine-grained light field QA, while identifying key limitations of current objective metrics under emerging coding pipelines. The subjectively annotated dataset is publicly available at https://plenodb.jpeg.org/lfqa/objectivecfp.