Paper accepted @ QoMEX 2026

Title: Asymmetry-Aware No-Reference Video Quality Assessment via Dual-Region Temporal Modeling

Authors: MohammadAli Hamidi, Hadi Amirpour, Christian Timmerer, Luigi Atzori

Abstract: Saliency and semantic-driven asymmetric encoding enable significant bitrate savings while maintaining a comparable viewing experience. This paper presents a No-Reference (NR) Video Quality Assessment (VQA) model for evaluating Asymmetrically Encoded Videos (AEV), addressing challenges such as varying compression levels, scaling artifacts, and asymmetric encoding strategies. The proposed approach combines compression-aware features derived from Quantization Parameters (QPs) with spatio-temporal perceptual descriptors capturing blur, motion, and temporal consistency. A hybrid regression framework based on XGBoost and Ridge regression is employed, where a weighted ensemble improves overall performance. Experimental results conducted on the dataset provided by the QoMEX VQA-AEV Grand Challenge, evaluated under a Leave-One-Source-Out (LOSO) protocol, show that the proposed method outperforms state-of-the-art NR-VQA models in terms of correlation coefficients (Pearson and Spearman) and root mean square error (RMSE).