Learning Binary Residual Representations for Domain-specific Video Streaming

TL;DR

This paper introduces a domain-specific video streaming method that combines H.264 compression with a binary autoencoder to encode residual information, resulting in improved video quality at the same bandwidth.

Contribution

The paper proposes a novel pipeline using binary residual autoencoders to enhance domain-specific video compression beyond standard H.264 methods.

Findings

Consistent quality improvements over H.264 across multiple datasets

Effective binary residual encoding reduces residual information size

Improved low-latency streaming performance

Abstract

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.