ICML 2025

Synonymous Variational Inference for Perceptual Image Compression
Zijian Liang 1 Kai Niu 1 2 Changshuo Wang 1 Jin Xu 1 Ping Zhang 3
Abstract
Recent contributions of semantic information the-
ory reveal the set-element relationship between
semantic and syntactic information, represented
as synonymous relationships. In this paper, we
propose a synonymous variational inference (SVI)
method based on this synonymity viewpoint to re-
analyze the perceptual image compression prob-
lem. It takes perceptual similarity as a typical syn-
onymous criterion to build an ideal synonymous
set (Synset), and approximate the posterior of its
latent synonymous representation with a paramet-
ric density by minimizing a partial semantic KL
divergence. This analysis theoretically proves
that the optimization direction of perception im-
age compression follows a triple tradeoff that
can cover the existing rate-distortion-perception
schemes. Additionally, we introduce synonymous
image compression (SIC), a new image compres-
sion scheme that corresponds to the analytical
process of SVI, and implement a progressive
SIC codec to fully leverage the model’s capabili-
ties. Experimental results demonstrate compara-
ble rate-distortion-perception performance using
a single progressive SIC codec, thus verifying the
effectiveness of our proposed analysis method.
1. Introduction
Image compression is a typical topic for lossy source cod-
ing, aiming to achieve the optimal tradeoff between recon-
structed image quality and coding rate. Following the rate-
distortion optimization instructed by Shannon’s classic in-
formation theory (1948; 1959), traditional image compres-
sion like JPEG (Wallace, 1991) and BPG (Bellard, 2015)
1Key Laboratory of Universal Wireless Communications, Min-
istry of Education, Beijing University of Posts and Telecommuni-
cations, Beijing, China 2Peng Cheng Laboratory, Shenzhen, China
3State Key Laboratory of Networking and Switching Technol-
ogy, Beijing University of Posts an...