Papers for

image compression developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Rate distortion perception framework guides better image and video coding

An Overview of Rate-Distortion-Perception Theory

Abstract: This paper provides a comprehensive overview of the rate-distortion-perception framework, tracing its evolution from mathematical theory to practical deployment. We review the mathematical definition of the the mathematical definition of the Blau--Michaeli function and the associated coding theorems, examine computational methods for its evaluation, and discuss alternative formulations of the framework. Operational principles and design guidelines for modern image and video coding are also presented, highlighting the rate-distortion-perception framework as a rigorous foundation for developing next-generation perceptual compression systems.

Mon 14 SeptInformation Theory
The gist
When pictures and videos are compressed, it's important to keep them looking good while using less space. This paper explains a way to balance file size, quality, and how natural images look using mathematical ideas. The authors review tools for measuring and improving this balance and suggest how to design better compression systems. This helps make videos and images smaller without making them look worse to people.
Open → 2609.15580v1

Successive refinement coding improves perception with common randomness

Successive Refinement Under Strong-Sense Perfect Perception

Abstract: We revisit a multiterminal lossy source coding problem named successive refinement and derive the rate-distortion-perception region under the strong-sense perfect perception constraint in the presence of unlimited common randomness. Specifically, in successive refinement, one aims to compress a source sequence and allows two distinct decoders to recover the source sequence at different distortion levels. By imposing the strong-sense perfect perception constraint, our results refine the previous result by analyzing the impact of the perceptual quality. Our achievability proof is inspired by output constrained lossy source coding and our converse proof adapts the proof steps of the standard successive refinement problem. Furthermore, we provide a numerical example of the Bernoulli source to illustrate our result and show that the Bernoulli source under Hamming distortion is successively refinable even with the strong-sense perfect perception constraint.

Sun 13 SeptInformation Theory
The gist
This paper studies how to compress information so that two users can recover it at different quality levels, focusing on making the recovered data look perfectly natural to human perception. The authors examine how adding a strong perception constraint affects compression, using a scenario where shared randomness is unlimited. They prove that for a simple type of data called a Bernoulli source, good compression is possible without harming perceptual quality. This work helps clarify how to balance data compression, quality, and natural appearance together.
Open → 2609.14404v1