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A New HDR Video Reconstruction Benchmark, Dataset and Metricстатья
- Авторы: Voronin Mikhail, Safonov Nikolay, Vatolin Dmitriy
- Сборник: Proceedings of the 2024 8th International Conference on Digital Signal Processing
- Серия: ICDSP ’24
- Год издания: 2024
- Издательство: Association for Computing Machinery
- Местоположение издательства: New York, N.Y., United States
- Первая страница: 27
- Последняя страница: 32
- DOI: 10.1145/3653876.3653896
- Аннотация: This paper discusses the challenges of evaluating SDR-to-HDR methods. The lack of comprehensive comparisons between existing methods prompted us to independently evaluate 14 HDR reconstruction approaches. To achieve this, we created a new closed HDR dataset. The evaluation involves objective quality metrics, such as PSNR, SSIM, MS-SSIM and NIQE adapted for HDR content. The study presents the first publicly available subjective scores for HDR reconstruction methods. The perceived quality of the reconstructed HDR videos is assessed by conducting a pairwise comparison. The research shows the strengths and weaknesses of each method, offering valuable insights for HDR video reconstruction and highlighting the need for improved quality assessment metrics in this domain. In addition, we introduce a novel quality evaluation metric, which leverages machine learning techniques to measure the effectiveness of HDR reconstruction methods.
- Добавил в систему: Воронин Михаил Яковлевич