The FilmGrainStyle740k dataset is a set of image pairs of grain-free and grainy images. Images with grain were generated from the clean images, using some proprietary plug-in in the VFX and Film Editing Software Nuke, that allows to adjust the grain patterns to the underlying content according to three attributes from the original images: intensity, texture and saturation. This plugin was elaborated with support from professional colorists. For each grain-free image, five grainy versions were generated.

The dataset is intended to be used for training deep learning models for film grain analysis, film grain removal and synthesis, film grain detection, film grain intensity prediction, film grain quality assessment, etc. A detailed description of the benchmark can be found on our Data Description page. The license conditions are mentioned on the Download page.

This Dataset comes from a collaboration with the IETR Lab (

All documents and papers that report on research that uses the FilmGrainStyle740k Dataset must acknowledge the use of the dataset by including an appropriate citation to the following:

Zoubida Ameur, Claire-Hélène Demarty, Olivier Le Meur, Daniel Ménard and Edouard François. "Style-based film grain analysis and synthesis." ACM Multimedia Systems (2023).

It would be highly appreciated if this use was shared with InterDigital, at this address: 

filmgrainstylemanagement@interdigital.com

, with “FilmGrainStyle740k” in the subject of your email.

The FilmGrainStyle740k Dataset contains both grain-free and grainy images.

Grain-free images come from large and different image datasets including: 

 [5] datasets counting in total 148694 image samples. Images from these sets are collected from websites for sharing photos and videos, while paying special attention to the image quality, to the diversity of sources, to the image contents and to their associated copyrights.

Grainy images are obtained by adding film grain with different characteristics to the grain-free images. Film grain is added using some proprietary plug-in in the VFX and Film Editing Software Nuke, that allows to adjust the grain patterns to the underlying content according to three attributes from the original images: intensity, texture and saturation. This plugin was elaborated with support from professional colorists.

Varying the parameters according to Table 1, we were able to generate 240 different grain combinations/styles in total. For each clean image, 5 different grainy versions were produced such that all 240 grain styles are represented in an equitable manner in the dataset. In total, our dataset contains 740k grainy images.

Lin, Hanhe, Vlad Hosu, and Dietmar Saupe. "KADID-10k: A large-scale artificially distorted IQA database." 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2019.

Lim, Bee, et al. "Enhanced deep residual networks for single image super-resolution." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017.

Agustsson, Eirikur, and Radu Timofte. "Ntire 2017 challenge on single image super-resolution: Dataset and study." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017.

Martin, David, et al. "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics." Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. Vol. 2. IEEE, 2001.

Ma, Kede, et al. "Waterloo exploration database: New challenges for image quality assessment models." IEEE Transactions on Image Processing 26.2 (2016): 1004-1016.

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You will receive instructions on how to download the dataset via this email address. We may store the data you supplied in order to contact you later about benchmark related matters. The data will not be used in any other way.

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Their corresponding grainy versions for 5 different sets of parameters.

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