We introduce HIDRO-VQA, a no-reference (NR) video quality assessment model designed to provide precise quality evaluations of High Dynamic Range (HDR) videos. HDR videos exhibit a broader spectrum of luminance, detail, and color than Standard Dynamic Range (SDR) videos. As HDR content becomes increasingly popular, there is a growing demand for video quality assessment (VQA) algorithms that effectively address distortions unique to HDR content. To address this challenge, we propose a self-supervised contrastive fine-tuning approach to transfer quality-aware features from the SDR to the HDR domain, utilizing unlabeled HDR videos. Our findings demonstrate that self-supervised pre-trained neural networks on SDR content can be further fine-tuned in a self-supervised setting using limited unlabeled HDR videos to achieve state-of-the-art performance on the only publicly available VQA database for HDR content, the LIVE-HDR VQA database. Moreover, our algorithm can be extended to the Full Reference VQA setting, also achieving state-of-the-art performance. Our code is available publicly at https://github.com/avinabsaha/HIDRO-VQA.

翻译：我们提出HIDRO-VQA，一种无参考（NR）视频质量评估模型，旨在为高动态范围（HDR）视频提供精准质量评价。相较于标准动态范围（SDR）视频，HDR视频呈现更宽广的亮度、细节与色彩范围。随着HDR内容日益普及，针对HDR特有失真进行有效处理的视频质量评估（VQA）算法需求不断增长。为应对这一挑战，我们提出一种基于自监督对比微调的方法，通过利用无标注HDR视频，将SDR领域的质量感知特征迁移至HDR域。研究结果表明，基于SDR内容进行自监督预训练的神经网络，可通过有限无标注HDR视频在自监督场景下进一步微调，从而在目前唯一的公开HDR视频质量评估数据库——LIVE-HDR VQA数据库上达到最优性能。此外，我们的算法可扩展至全参考VQA场景，同样实现最优性能。相关代码已在https://github.com/avinabsaha/HIDRO-VQA 开源。