# 3R-INN: How to be climate friendly while consuming/delivering videos?

arXiv: https://arxiv.org/abs/2403.11760 · HTML: https://mlsanity.com/paper/2403.11760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [Invertible Image Rescaling](https://mlsanity.com/paper/2005.05650/) (2020)
- [Invertible Denoising Network: A Light Solution for Real Noise Removal](https://mlsanity.com/paper/2104.10546/) (2021)
- [Learned Image Downscaling for Upscaling using Content Adaptive Resampler](https://mlsanity.com/paper/1907.12904/) (2019)
- [Blind Image Denoising via Dependent Dirichlet Process Tree](https://mlsanity.com/paper/1601.03117/) (2016)
- [Deep-based Film Grain Removal and Synthesis](https://mlsanity.com/paper/2206.07411/) (2022)
- [Versatile video coding and super-resolution for efficient delivery of 8K video with 4K backward-compatibility](https://mlsanity.com/paper/2002.06922/) (2020)
- [Sweet Streams are Made of This: The System Engineer's View on Energy Efficiency in Video Communications](https://mlsanity.com/paper/2209.15405/) (2022)
- [Invertible Mosaic Image Hiding Network for Very Large Capacity Image Steganography](https://mlsanity.com/paper/2309.08987/) (2023)
- [Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond](https://mlsanity.com/paper/2301.13358/) (2023)

## Built on by (most cited first, 1 of 1)

- [FGA-NN: Film Grain Analysis Neural Network](https://mlsanity.com/paper/2506.14350/) (2025)

## Abstract (arXiv)

The consumption of a video requires a considerable amount of energy during the various stages of its life-cycle. With a billion hours of video consumed daily, this contributes significantly to the greenhouse gas emission. Therefore, reducing the end-to-end carbon footprint of the video chain, while preserving the quality of experience at the user side, is of high importance. To contribute in an impactful manner, we propose 3R-INN, a single light invertible network that does three tasks at once: given a high-resolution grainy image, it Rescales it to a lower resolution, Removes film grain and Reduces its power consumption when displayed. Providing such a minimum viable quality content contributes to reducing the energy consumption during encoding, transmission, decoding and display. 3R-INN also offers the possibility to restore either the high-resolution grainy original image or a grain-free version, thanks to its invertibility and the disentanglement of the high frequency, and without transmitting auxiliary data. Experiments show that, while enabling significant energy savings for encoding (78%), decoding (77%) and rendering (5% to 20%), 3R-INN outperforms state-of-the-art film grain synthesis and energy-aware methods and achieves state-of-the-art performance on the rescaling task on different test-sets.