# Towards Transparent Application of Machine Learning in Video Processing

arXiv: https://arxiv.org/abs/2105.12700 · HTML: https://mlsanity.com/paper/2105.12700/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Deep Learning-Based Video Coding: A Review and A Case Study](https://mlsanity.com/paper/1904.12462/) (2019)
- [End-to-End Conditional GAN-based Architectures for Image Colourisation](https://mlsanity.com/paper/1908.09873/) (2019)
- [Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding](https://mlsanity.com/paper/2006.06392/) (2020)
- [Analytic Simplification of Neural Network based Intra-Prediction Modes for Video Compression](https://mlsanity.com/paper/2004.11056/) (2020)
- [Chroma Intra Prediction with attention-based CNN architectures](https://mlsanity.com/paper/2006.15349/) (2020)
- [Decision Trees for Complexity Reduction in Video Compression](https://mlsanity.com/paper/1908.04168/) (2019)
- [Estimation of Rate Control Parameters for Video Coding Using CNN](https://mlsanity.com/paper/2003.06315/) (2020)

## Abstract (arXiv)

Machine learning techniques for more efficient video compression and video enhancement have been developed thanks to breakthroughs in deep learning. The new techniques, considered as an advanced form of Artificial Intelligence (AI), bring previously unforeseen capabilities. However, they typically come in the form of resource-hungry black-boxes (overly complex with little transparency regarding the inner workings). Their application can therefore be unpredictable and generally unreliable for large-scale use (e.g. in live broadcast). The aim of this work is to understand and optimise learned models in video processing applications so systems that incorporate them can be used in a more trustworthy manner. In this context, the presented work introduces principles for simplification of learned models targeting improved transparency in implementing machine learning for video production and distribution applications. These principles are demonstrated on video compression examples, showing how bitrate savings and reduced complexity can be achieved by simplifying relevant deep learning models.