A Compression Objective and a Cycle Loss for Neural Image Compression

TL;DR

This paper introduces a compression objective and cycle loss for neural image compression, improving perceptual quality and control over distortion in autoencoder-based methods.

Contribution

It proposes novel objective terms for neural image compression that enhance perceptual quality and control over distortion, combining sparsity, low entropy, and cycle consistency.

Findings

Cycle loss improves perceptual quality of compressed images.

Different training objectives lead to different points on the perception-distortion curve.

Autoencoders trained with cycle loss produce images with higher perceptual quality.

Abstract

In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss. These terms are applied on the encoder output of an autoencoder and are used in combination with reconstruction losses. The compression objective encourages sparsity and low entropy in the activations. The cycle loss term represents the distortion between encoder outputs computed from the original image and from the reconstructed image (code-domain distortion). We train different autoencoders by using the compression objective in combination with different losses: a) MSE, b) MSE and MSSSIM, c) MSE, MS-SSIM and cycle loss. We observe that images encoded by these differently-trained autoencoders fall into different points of the perception-distortion curve (while having similar bit-rates). In particular, MSE-only training favors low image-domain distortion, whereas cycle loss training favors high perceptual quality.