7 papers
Perception-based Image Denoising via Generative Compression
Nam Nguyen, Thinh Nguyen, Bella Bose
Image denoising aims to remove noise while preserving structural details and perceptual realism, yet distortion-driven methods often produce over-smoothed reconstructions, especial…
Rate-Distortion-Classification Representation Theory for Bernoulli Sources
Nam Nguyen, Thinh Nguyen, Bella Bose
We study task-oriented lossy compression through the lens of rate-distortion-classification (RDC) representations. The source is Bernoulli, the distortion measure is Hamming, and t…
Cross-Domain Lossy Compression via Constrained Minimum Entropy Coupling
Nam Nguyen, Hassan Tavakoli, An Vuong +2
This paper studies cross-domain lossy compression through the lens of minimum entropy coupling (MEC) with rate and classification constraints. In this setting, an encoder observes…
Universal Rate-Distortion-Classification Representations for Lossy Compression
Nam Nguyen, Thuan Nguyen, Thinh Nguyen +1
In lossy compression, Wang et al. [1] recently introduced the rate-distortion-perception-classification function, which supports multi-task learning by jointly optimizing perceptua…
A Theory of Universal Rate-Distortion-Classification Representations for Lossy Compression
Nam Nguyen, Thinh Nguyen, Bella Bose
In lossy compression, Blau and Michaeli [5] introduced the information rate-distortion-perception (RDP) function, extending traditional rate-distortion theory by incorporating perc…
Universal Representations for Classification-enhanced Lossy Compression
Nam Nguyen
In lossy compression, the classical tradeoff between compression rate and reconstruction distortion has traditionally guided algorithm design. However, Blau and Michaeli [5] introd…