Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videos

• URL: http://arxiv.org/abs/2603.00938v1
• Date: Sun, 01 Mar 2026 06:02:40 GMT
• Title: Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videos
• Authors: Shreshth Saini, Bowen Chen, Neil Birkbeck, Yilin Wang, Balu Adsumilli, Alan C. Bovik,
• Abstract summary: High Dynamic Range (UGC) user-generated (UGC) videos are rapidly proliferating across social platforms.<n>Most perceptual video quality assessment (VQA) systems remain tailored to Standard Dynamic Range (SDR) models.<n>We introduce HDR-Q, the first Multimodal Large Language Model (MLLM) for HDR-UGC VQA.
• Score: 40.03485113183691
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High Dynamic Range (HDR) user-generated (UGC) videos are rapidly proliferating across social platforms, yet most perceptual video quality assessment (VQA) systems remain tailored to Standard Dynamic Range (SDR). HDR has a higher bit depth, wide color gamut, and elevated luminance range, exposing distortions such as near-black crushing, highlight clipping, banding, and exposure flicker that amplify UGC artifacts and challenge SDR models. To catalyze progress, we curate Beyond8Bits, a large-scale subjective dataset of 44K videos from 6.5K sources with over 1.5M crowd ratings, spanning diverse scenes, capture conditions, and compression settings. We further introduce HDR-Q, the first Multimodal Large Language Model (MLLM) for HDR-UGC VQA. We propose (i) a novel HDR-aware vision encoder to produce HDR-sensitive embeddings, and (ii) HDR-Aware Policy Optimization (HAPO), an RL finetuning framework that anchors reasoning to HDR cues. HAPO augments GRPO via an HDR-SDR contrastive KL that encourages token reliance on HDR inputs and a Gaussian weighted regression reward for fine-grained MOS calibration. Across Beyond8Bits and public HDR-VQA benchmarks, HDR-Q delivers state-of-the-art performance.

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