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HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment
- Publication Year :
- 2023
-
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.<br />Comment: WACV 2024 Workshop Paper. Shreshth Saini, Avinab Saha contributed equally to this work
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2311.11059
- Document Type :
- Working Paper