# RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training

arXiv: https://arxiv.org/abs/2202.08595 · HTML: https://mlsanity.com/paper/2202.08595/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2022.

## Built on by (most cited first, 14 of 14)

- [Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion](https://mlsanity.com/paper/2408.07303/) (2024)
- [RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agents](https://mlsanity.com/paper/2410.13384/) (2024)
- [AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results](https://mlsanity.com/paper/2404.16205/) (2024)
- [ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment](https://mlsanity.com/paper/2407.11496/) (2024)
- [BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos](https://mlsanity.com/paper/2312.08859/) (2023)
- [UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment](https://mlsanity.com/paper/2308.06853/) (2023)
- [RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content](https://mlsanity.com/paper/2405.08621/) (2024)
- [MVAD: A Multiple Visual Artifact Detector for Video Streaming](https://mlsanity.com/paper/2406.00212/) (2024)
- [BVI-UGC: A Video Quality Database for User-Generated Content Transcoding](https://mlsanity.com/paper/2408.07171/) (2024)
- [CAMP-VQA: Caption-Embedded Multimodal Perception for No-Reference Quality Assessment of Compressed Video](https://mlsanity.com/paper/2511.07290/) (2025)
- [RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment](https://mlsanity.com/paper/2312.08864/) (2023)
- [Causal Debiasing for Visual Commonsense Reasoning](https://mlsanity.com/paper/2510.20281/) (2025)
- [A Mamba-based Perceptual Loss Function for Learning-based UGC Transcoding](https://mlsanity.com/paper/2603.25566/) (2026)
- [FDIM: A Feature-distance-based Generic Video Quality Metric for Versatile Codecs](https://mlsanity.com/paper/2604.24123/) (2026)

## Abstract (arXiv)

In recent years, deep learning techniques have shown significant potential for improving video quality assessment (VQA), achieving higher correlation with subjective opinions compared to conventional approaches. However, the development of deep VQA methods has been constrained by the limited availability of large-scale training databases and ineffective training methodologies. As a result, it is difficult for deep VQA approaches to achieve consistently superior performance and model generalization. In this context, this paper proposes new VQA methods based on a two-stage training methodology which motivates us to develop a large-scale VQA training database without employing human subjects to provide ground truth labels. This method was used to train a new transformer-based network architecture, exploiting quality ranking of different distorted sequences rather than minimizing the difference from the ground-truth quality labels. The resulting deep VQA methods (for both full reference and no reference scenarios), FR- and NR-RankDVQA, exhibit consistently higher correlation with perceptual quality compared to the state-of-the-art conventional and deep VQA methods, with average SROCC values of 0.8972 (FR) and 0.7791 (NR) over eight test sets without performing cross-validation. The source code of the proposed quality metrics and the large training database are available at https://chenfeng-bristol.github.io/RankDVQA.