# Learning Binary Residual Representations for Domain-specific Video Streaming

arXiv: https://arxiv.org/abs/1712.05087 · HTML: https://mlsanity.com/paper/1712.05087/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2017.

## Built on by (most cited first, 7 of 7)

- [FSDR: Frequency Space Domain Randomization for Domain Generalization](https://mlsanity.com/paper/2103.02370/) (2021)
- [A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning](https://mlsanity.com/paper/2110.01515/) (2021)
- [Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking](https://mlsanity.com/paper/2104.03114/) (2021)
- [Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms](https://mlsanity.com/paper/2105.05791/) (2021)
- [Viewport-based Neural 360deg Image Compression](https://mlsanity.com/paper/2603.22776/) (2026)
- [Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers](https://mlsanity.com/paper/2112.03487/) (2021)
- [CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability](https://mlsanity.com/paper/2202.00416/) (2022)

## Abstract (arXiv)

We study domain-specific video streaming. Specifically, we target a streaming setting where the videos to be streamed from a server to a client are all in the same domain and they have to be compressed to a small size for low-latency transmission. Several popular video streaming services, such as the video game streaming services of GeForce Now and Twitch, fall in this category. While conventional video compression standards such as H.264 are commonly used for this task, we hypothesize that one can leverage the property that the videos are all in the same domain to achieve better video quality. Based on this hypothesis, we propose a novel video compression pipeline. Specifically, we first apply H.264 to compress domain-specific videos. We then train a novel binary autoencoder to encode the leftover domain-specific residual information frame-by-frame into binary representations. These binary representations are then compressed and sent to the client together with the H.264 stream. In our experiments, we show that our pipeline yields consistent gains over standard H.264 compression across several benchmark datasets while using the same channel bandwidth.