The Vid3oC and IntVID datasets for video super resolution and quality mapping
The Vid3oC and IntVID datasets for video super resolution and quality mapping
The current rapid advancements of computational hardware has opened the door for deep networks to be applied for real-time video processing, even on consumer devices. Appealing tasks include video super-resolution, compression artifact removal, and quality enhancement. These problems require high-quality datasets that can be applied for training and benchmarking. In this work, we therefore introduce two video datasets, aimed for a variety of tasks. First, we propose the Vid3oC dataset, containing 82 simultaneous recordings of 3 camera sensors. It is recorded with a multi-camera rig, including a high-quality DSLR camera, a high-end smartphone, and a stereo camera sensor. Second, we introduce the IntVID dataset, containing over 150 high-quality videos crawled from the internet. The datasets were employed for the AIM 2019 challenges for video super-resolution and quality mapping.
3609-3616
Kim, Sohyeong
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Li, Guanju
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Fuoli, Dario
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Danelljan, Martin
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Huang, Zhiwu
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Gu, Shuhang
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Timofte, Radu
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1 January 2019
Kim, Sohyeong
e7b58828-09b1-48ab-b3c7-798f67bcf2ab
Li, Guanju
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Fuoli, Dario
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Danelljan, Martin
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Huang, Zhiwu
84f477cd-9097-44dd-a33e-ff71f253d36b
Gu, Shuhang
8503a4e5-7e8d-42bf-bf16-48448b795254
Timofte, Radu
848d4025-8613-43f3-92b7-4b4a2b29711a
Kim, Sohyeong, Li, Guanju, Fuoli, Dario, Danelljan, Martin, Huang, Zhiwu, Gu, Shuhang and Timofte, Radu
(2019)
The Vid3oC and IntVID datasets for video super resolution and quality mapping.
International Conference on Computer Vision (ICCV) workshop.
.
(doi:10.1109/ICCVW.2019.00446).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The current rapid advancements of computational hardware has opened the door for deep networks to be applied for real-time video processing, even on consumer devices. Appealing tasks include video super-resolution, compression artifact removal, and quality enhancement. These problems require high-quality datasets that can be applied for training and benchmarking. In this work, we therefore introduce two video datasets, aimed for a variety of tasks. First, we propose the Vid3oC dataset, containing 82 simultaneous recordings of 3 camera sensors. It is recorded with a multi-camera rig, including a high-quality DSLR camera, a high-end smartphone, and a stereo camera sensor. Second, we introduce the IntVID dataset, containing over 150 high-quality videos crawled from the internet. The datasets were employed for the AIM 2019 challenges for video super-resolution and quality mapping.
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Published date: 1 January 2019
Venue - Dates:
International Conference on Computer Vision (ICCV) workshop, 2019-10-27
Identifiers
Local EPrints ID: 501677
URI: http://eprints.soton.ac.uk/id/eprint/501677
PURE UUID: bd1125b1-9d53-4703-8097-56ed12147604
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Date deposited: 05 Jun 2025 16:57
Last modified: 06 Jun 2025 02:06
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Contributors
Author:
Sohyeong Kim
Author:
Guanju Li
Author:
Dario Fuoli
Author:
Martin Danelljan
Author:
Zhiwu Huang
Author:
Shuhang Gu
Author:
Radu Timofte
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