# Learning Quality from Complexity and Structure: A Feature-Fused XGBoost Model for Video Quality Assessment

arXiv: https://arxiv.org/abs/2506.09795 · HTML: https://mlsanity.com/paper/2506.09795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [ICME 2025 Generalizable HDR and SDR Video Quality Measurement Grand Challenge](https://mlsanity.com/paper/2506.22790/) (2025)

## Abstract (arXiv)

This paper presents a novel approach for reduced-reference video quality assessment (VQA), developed as part of the recent VQA Grand Challenge. Our method leverages low-level complexity and structural information from reference and test videos to predict perceptual quality scores. Specifically, we extract spatio-temporal features using Video Complexity Analyzer (VCA) and compute SSIM values from the test video to capture both texture and structural characteristics. These features are aggregated through temporal pooling, and residual features are calculated by comparing the original and distorted feature sets. The combined features are used to train an XGBoost regression model that estimates the overall video quality. The pipeline is fully automated, interpretable, and highly scalable, requiring no deep neural networks or GPU inference. Experimental results on the challenge dataset demonstrate that our proposed method achieves competitive correlation with subjective quality scores while maintaining a low computational footprint. The model's lightweight design and strong generalization performance suit real-time streaming quality monitoring and adaptive encoding scenarios.