# Energy-Aware Frame Rate Selection for Video Coding

arXiv: https://arxiv.org/abs/2603.18305 · HTML: https://mlsanity.com/paper/2603.18305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [The Bjontegaard Bible -- Why your Way of Comparing Video Codecs May Be Wrong](https://mlsanity.com/paper/2304.12852/) (2023)
- [Space-Time Video Regularity and Visual Fidelity: Compression, Resolution and Frame Rate Adaptation](https://mlsanity.com/paper/2103.16771/) (2021)
- [Sweet Streams are Made of This: The System Engineer's View on Energy Efficiency in Video Communications](https://mlsanity.com/paper/2209.15405/) (2022)
- [Video Decoding Energy Reduction Using Temporal-Domain Filtering](https://mlsanity.com/paper/2306.06917/) (2023)
- [Quality-driven Variable Frame-Rate for Green Video Coding in Broadcast Applications](https://mlsanity.com/paper/2012.14796/) (2020)
- [Modeling the HEVC Encoding Energy Using the Encoder Processing Time](https://mlsanity.com/paper/2207.02676/) (2022)
- [Matched Quality Evaluation of Temporally Downsampled Videos with Non-Integer Factors](https://mlsanity.com/paper/2209.10353/) (2022)
- [Power-Efficient Video Streaming on Mobile Devices Using Optimal Spatial Scaling](https://mlsanity.com/paper/2307.08337/) (2023)
- [Video Decoding Energy Estimation Using Processor Events](https://mlsanity.com/paper/2307.14000/) (2023)
- [Towards Video Codec Performance Evaluation: A Rate-Energy-Distortion Perspective](https://mlsanity.com/paper/2405.17866/) (2024)

## Abstract (arXiv)

The main contributions of this paper are twofold: First, we present an in-depth analysis of the impact of frame rate reductions on the visual quality of the video and the encoding as well as decoding energy. Second, we propose a lightweight frame rate selection method for energy- and quality-aware encoding. Concerning the first contribution, this paper performs extensive encoding and decoding measurements, followed by an investigation of the impact of temporal downsampling on the energy demand of encoding and decoding at different frame rates. Furthermore, we determine the objective visual quality of the downsampled videos. As a result of this investigation, we identify content- and quantization-setting-dependent energy-aware frame rates, i.e., the temporal downsampling factors that lead to Pareto-optimality in terms of energy and quality. We demonstrate that significant energy savings are achieved while maintaining constant visual quality. Subsequently, a subjective experiment is conducted to verify this observation regarding perceptual quality using mean opinion scores. As the second contribution, we propose an energy-aware frame rate selection method that extracts spatio-temporal features from the video sequences. Based on these features, the proposed method employs a feature-based supervised machine learning approach to predict energy-aware frame rates for a given quantization parameter and video sequence, aiming to reduce energy consumption during encoding and decoding. The experimental results demonstrate that the proposed method offers significant energy savings, with an average of 17.46% and 17.60% of encoding and decoding energy demand reduction, respectively, alongside 3.38% average bitrate savings at a constant quality.