HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment

• URL: http://arxiv.org/abs/2311.11059v2
• Date: Wed, 20 Dec 2023 07:58:43 GMT
• Title: HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment
• Authors: Shreshth Saini, Avinab Saha, Alan C. Bovik
• Abstract summary: We introduce HIDRO-VQA, a no-reference (NR) video quality assessment model designed to provide precise quality evaluations of High Dynamic Range () videos. Our findings demonstrate that self-supervised pre-trained neural networks can be further fine-tuned in a self-supervised setting to achieve state-of-the-art performance. Our algorithm can be extended to the Full Reference VQA setting, also achieving state-of-the-art performance.
• Score: 36.1179702443845
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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