Synonymous Variational Inference for Perceptual Image Compression
Zijian Liang, Kai Niu, Changshuo Wang, Jin Xu, Ping Zhang

TL;DR
This paper introduces a novel variational inference framework based on semantic synonymity for perceptual image compression, providing theoretical insights and a new compression scheme that balances rate, distortion, and perception effectively.
Contribution
It proposes a synonymous variational inference approach that re-analyzes perceptual image compression through semantic synonymity, leading to a new compression scheme and theoretical understanding.
Findings
Theoretical proof of the perception compression tradeoff.
Development of a progressive SIC codec.
Comparable rate-distortion-perception performance achieved.
Abstract
Recent contributions of semantic information theory 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 problem. It takes perceptual similarity as a typical synonymous criterion to build an ideal synonymous set (Synset), and approximate the posterior of its latent synonymous representation with a parametric density by minimizing a partial semantic KL divergence. This analysis theoretically proves that the optimization direction of perception image compression follows a triple tradeoff that can cover the existing rate-distortion-perception schemes. Additionally, we introduce synonymous image compression (SIC), a new image compression scheme that corresponds to the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
Taxonomy
TopicsAdvanced Data Compression Techniques · Generative Adversarial Networks and Image Synthesis · Wireless Signal Modulation Classification
MethodsVariational Inference · Sparse Evolutionary Training
