arXiv Machine Learning

A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff

arXiv:2604. 14603v2 Announce Type: replace-cross Abstract: The fundamental limit of natural signal compression has traditionally been characterized by classical rate-distortion (RD) theory through the tradeoff between coding rate and reconstruction distortion, while the rate-distortion-perception (RDP) framework introduces a divergence-based measure of perceptual quality as a modeling principle, leaving its theoretical origin unclear.

arXiv Machine Learning
Sep 3

A Unified Rate-Distortion Perspective on Vector, Product, and Scalar Quantization

The paper introduces a unified rate–distortion framework for discrete visual tokenization, encompassing vector, product, and scalar quantization. It shows that minimizing distortion, rather than maximizing codebook utilization, is the key objective for reconstruction fidelity and establishes fairness conditions for comparing quantizers. Under these conditions, the study confirms the distortion hierarchy VQ–PQ–SQ and demonstrates that modern VQ methods achieve the lowest distortion.

By Xianghong Fang, Wenlong Mou, Yuan Yuan, Dehan Kong, Tim G. J. Rudner
arXiv Computer Vision
3d ago

Rethinking Generative Image Compression at Extremely Low Bitrates

The paper introduces RAE-CoD, a diffusion-based compression method that operates in a representation autoencoder space to preserve recognizable content even at extremely low bitrates. It addresses the problem of semantic collapse observed in existing codecs when the bitrate approaches zero, showing that reconstruction losses conflict with semantic objectives and that VAE diffusion models lose efficiency in preserving semantics. Experiments on MSCOCO-30K demonstrate that RAE-CoD outperforms competitors, reducing VFM feature MSE and Fréchet Distance ratios by at least 25.7% and 69.1% at 0.001–0.008 bpp while maintaining stable recognizability and quality.

By Tianyu Zhang, Zhaoyang Jia, Houqiang Li, Dong Liu
arXiv Machine Learning
Jun 2

How Neural Losses Shape VAE Latents

arXiv:2606. 00635v1 Announce Type: new Abstract: Modern VAEs are rarely trained with the pointwise likelihood implied by the standard $\beta$-VAE objective.

By Giorgio Strano, Luca Cerovaz, Michele Mancusi, Tommaso Mencattini, Emanuele Rodol\`a
arXiv Computer Vision
Sep 24

Information Capacity of Generative Video Compression: Quantifying the Rate-Compute Exchange at Identical Quality

The paper introduces the concept of Information Capacity (IC) to quantify how much bandwidth savings a unit of decoder compute can achieve in generative video compression (GVC). By modeling reconstruction quality as a two‑factor power law in data rate and compute, the authors fit measured DISTS of two GVC decoders with high accuracy and define IC as the negative logarithmic slope along an iso‑quality contour. IC is dimensionless, enabling architecture‑agnostic comparisons and revealing that a 14B decoder trades compute for rate far more efficiently than a 1.3B decoder, with significant variation across datasets.

By Cheng Yuan, Jiawei Shao, Xuelong Li