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The paper introduces MTL-VQA, a multi-task learning framework for no-reference video quality assessment (NR-VQA) of gaming videos, addressing the scarcity of human-rated data by leveraging full-reference (FR) metrics as supervisory signals. By adaptively weighting and jointly optimizing multiple FR objectives during pretraining, the method learns shared perceptual representations relevant to video quality. Experiments demonstrate that MTL-VQA achieves competitive performance compared to state-of-the-art NR-VQA methods in both MOS-supervised and label-efficient settings.
Skip the expensive human ratings: MTL-VQA uses full-reference metrics to pretrain perceptual representations for gaming video quality assessment, rivaling state-of-the-art no-reference methods.
No-reference video quality assessment (NR-VQA) for gaming videos is challenging due to limited human-rated datasets and unique content characteristics including fast motion, stylized graphics, and compression artifacts. We present MTL-VQA, a multi-task learning framework that uses full-reference metrics as supervisory signals to learn perceptually meaningful features without human labels for pretraining. By jointly optimizing multiple full-reference (FR) objectives with adaptive task weighting, our approach learns shared representations that transfer effectively to NR-VQA. Experiments on gaming video datasets show MTL-VQA achieves performance competitive with state-of-the-art NR-VQA methods across both MOS-supervised and label-efficient/self-supervised settings.