Rate-Distortion in Image Coding for Machines

TL;DR

This paper investigates how to optimize image compression for both human viewing and machine analysis, demonstrating that matching features from deeper neural network layers improves rate-distortion performance.

Contribution

It introduces a theoretical proof using the data processing inequality and empirically validates that deeper layer feature matching enhances joint human-machine image coding.

Findings

Matching features from deeper layers yields better rate-distortion trade-offs.

Deeper layer feature matching improves scalability for human and machine tasks.

Empirical results confirm theoretical advantages of deeper layer selection.

Abstract

In recent years, there has been a sharp increase in transmission of images to remote servers specifically for the purpose of computer vision. In many applications, such as surveillance, images are mostly transmitted for automated analysis, and rarely seen by humans. Using traditional compression for this scenario has been shown to be inefficient in terms of bit-rate, likely due to the focus on human based distortion metrics. Thus, it is important to create specific image coding methods for joint use by humans and machines. One way to create the machine side of such a codec is to perform feature matching of some intermediate layer in a Deep Neural Network performing the machine task. In this work, we explore the effects of the layer choice used in training a learnable codec for humans and machines. We prove, using the data processing inequality, that matching features from deeper layers is preferable in the sense of rate-distortion. Next, we confirm our findings empirically by re-training an existing model for scalable human-machine coding. In our experiments we show the trade-off between the human and machine sides of such a scalable model, and discuss the benefit of using deeper layers for training in that regard.