  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.
