Efficient No-Reference Video Quality Assessment Using Video Masked Autoencoder Feature Mixing

作者：S. N, S. Channappayya · 发表于：Asilomar Conference on Signals, Systems and Computers · 年份：2025 · DOI：10.1109/ieeeconf67917.2025.11443711 · 研究领域：Computer Science

Video quality assessment (VQA) is a key component in video processing and analysis, particularly with the growing volume of user-generated content (UGC) uploaded to platforms like YouTube and social media. In this work, we present a lightweight No-Reference Video Quality Assessment (NR-VQA), which combines a Video Masked Autoencoder (VMAE) and a modified Multilayer Perceptron Mixer (MixVPR) for quality prediction. The proposed VMAE and MLP-Mixer are based on a combination of a regressor inspired by the MixVPR feature mixer. The experimental results show that the proposed method achieves an excellent trade-off between performance and complexity, demonstrating its suitability for real-world applications. The algorithm’s performance is tested across five UGC datasets: KoNViD-1k and LIVE VQC, LIVE QUalcomm, CVD2014, and YouTube UGC. This work builds on advances in deep learning, particularly in transformer-based architectures and feature-mixing techniques.