Learned Image Coding for Machines: A Content-Adaptive Approach

TL;DR

This paper introduces a content-adaptive finetuning method for learned image codecs that enhances compression efficiency for machine-to-machine communication, achieving significant bitrate savings over traditional codecs.

Contribution

It proposes an inference-time finetuning scheme for learned image codecs, improving compression efficiency specifically for machine consumption scenarios.

Findings

Average bitrate saving of -3.66% (BD-rate) over pretrained codec.

Significant bitrate saving of -9.85% at low bitrates.

Achieves -30.54% BD-rate improvement over VVC.

Abstract

Today, according to the Cisco Annual Internet Report (2018-2023), the fastest-growing category of Internet traffic is machine-to-machine communication. In particular, machine-to-machine communication of images and videos represents a new challenge and opens up new perspectives in the context of data compression. One possible solution approach consists of adapting current human-targeted image and video coding standards to the use case of machine consumption. Another approach consists of developing completely new compression paradigms and architectures for machine-to-machine communications. In this paper, we focus on image compression and present an inference-time content-adaptive finetuning scheme that optimizes the latent representation of an end-to-end learned image codec, aimed at improving the compression efficiency for machine-consumption. The conducted experiments show that our online finetuning brings an average bitrate saving (BD-rate) of -3.66% with respect to our pretrained image codec. In particular, at low bitrate points, our proposed method results in a significant bitrate saving of -9.85%. Overall, our pretrained-and-then-finetuned system achieves -30.54% BD-rate over the state-of-the-art image/video codec Versatile Video Coding (VVC).