# HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment

arXiv: https://arxiv.org/abs/2311.11059 · HTML: https://mlsanity.com/paper/2311.11059/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 69 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Colorization as a Proxy Task for Visual Understanding](https://mlsanity.com/paper/1703.04044/) (2017)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [Large-Scale Study of Perceptual Video Quality](https://mlsanity.com/paper/1803.01761/) (2018)
- [YouTube UGC Dataset for Video Compression Research](https://mlsanity.com/paper/1904.06457/) (2019)
- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild](https://mlsanity.com/paper/2304.00451/) (2023)
- [Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications](https://mlsanity.com/paper/1904.11176/) (2019)
- [A New Journey from SDRTV to HDRTV](https://mlsanity.com/paper/2108.07978/) (2021)
- [Subjective and Objective Quality Assessment of High Frame Rate Videos](https://mlsanity.com/paper/2007.11634/) (2020)
- [ChipQA: No-Reference Video Quality Prediction via Space-Time Chips](https://mlsanity.com/paper/2109.08726/) (2021)
- [CONVIQT: Contrastive Video Quality Estimator](https://mlsanity.com/paper/2206.14713/) (2022)
- [Subjective Assessment of High Dynamic Range Videos Under Different Ambient Conditions](https://mlsanity.com/paper/2209.10005/) (2022)
- [Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos](https://mlsanity.com/paper/2305.17260/) (2023)
- [Making Video Quality Assessment Models Robust to Bit Depth](https://mlsanity.com/paper/2304.13092/) (2023)
- [GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content](https://mlsanity.com/paper/2305.02422/) (2023)
- [HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos](https://mlsanity.com/paper/2304.13156/) (2023)

## Built on by (most cited first, 7 of 7)

- [A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos](https://mlsanity.com/paper/2312.08524/) (2023)
- [Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling](https://mlsanity.com/paper/2501.07087/) (2025)
- [CHUG: Crowdsourced User-Generated HDR Video Quality Dataset](https://mlsanity.com/paper/2510.09879/) (2025)
- [Latent Guidance in Diffusion Models for Perceptual Evaluations](https://mlsanity.com/paper/2506.00327/) (2025)
- [Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videos](https://mlsanity.com/paper/2603.00938/) (2026)
- [LumaFlux: Lifting 8-Bit Worlds to HDR Reality with Physically-Guided Diffusion Transformers](https://mlsanity.com/paper/2604.02787/) (2026)
- [FDIM: A Feature-distance-based Generic Video Quality Metric for Versatile Codecs](https://mlsanity.com/paper/2604.24123/) (2026)

## Abstract (arXiv)

We introduce HIDRO-VQA, a no-reference (NR) video quality assessment model designed to provide precise quality evaluations of High Dynamic Range (HDR) videos. HDR videos exhibit a broader spectrum of luminance, detail, and color than Standard Dynamic Range (SDR) videos. As HDR content becomes increasingly popular, there is a growing demand for video quality assessment (VQA) algorithms that effectively address distortions unique to HDR content. To address this challenge, we propose a self-supervised contrastive fine-tuning approach to transfer quality-aware features from the SDR to the HDR domain, utilizing unlabeled HDR videos. Our findings demonstrate that self-supervised pre-trained neural networks on SDR content can be further fine-tuned in a self-supervised setting using limited unlabeled HDR videos to achieve state-of-the-art performance on the only publicly available VQA database for HDR content, the LIVE-HDR VQA database. Moreover, our algorithm can be extended to the Full Reference VQA setting, also achieving state-of-the-art performance. Our code is available publicly at https://github.com/avinabsaha/HIDRO-VQA.