# Deep Residual Learning in the JPEG Transform Domain

## TL;DR

This paper presents a method to perform residual network inference directly in the JPEG transform domain, enabling faster image processing with minimal accuracy loss by leveraging JPEG's linearity and sparsity.

## Contribution

The authors introduce a novel approach for residual networks to operate in the JPEG domain, including a model conversion algorithm and approximation techniques for ReLU.

## Key findings

- JPEG domain processing speeds up image inference
- Minimal accuracy loss with JPEG domain methods
- Applicable to image classification tasks

## Abstract

We introduce a general method of performing Residual Network inference and learning in the JPEG transform domain that allows the network to consume compressed images as input. Our formulation leverages the linearity of the JPEG transform to redefine convolution and batch normalization with a tune-able numerical approximation for ReLu. The result is mathematically equivalent to the spatial domain network up to the ReLu approximation accuracy. A formulation for image classification and a model conversion algorithm for spatial domain networks are given as examples of the method. We show that the sparsity of the JPEG format allows for faster processing of images with little to no penalty in the network accuracy.