Simulated Annealing for JPEG Quantization

TL;DR

This paper introduces a simulated annealing method to optimize JPEG quantization tables, achieving better compression and image fidelity while maintaining standard compliance.

Contribution

It presents a novel simulated annealing approach to find improved JPEG quantization tables that outperform industry standards in compression and quality.

Findings

Reduced FSIM error by over 10%

Improved compression by over 20% at quality level 95

Achieved better image fidelity with standard-compliant tables

Abstract

JPEG is one of the most widely used image formats, but in some ways remains surprisingly unoptimized, perhaps because some natural optimizations would go outside the standard that defines JPEG. We show how to improve JPEG compression in a standard-compliant, backward-compatible manner, by finding improved default quantization tables. We describe a simulated annealing technique that has allowed us to find several quantization tables that perform better than the industry standard, in terms of both compressed size and image fidelity. Specifically, we derive tables that reduce the FSIM error by over 10% while improving compression by over 20% at quality level 95 in our tests; we also provide similar results for other quality levels. While we acknowledge our approach can in some images lead to visible artifacts under large magnification, we believe use of these quantization tables, or additional tables that could be found using our methodology, would significantly reduce JPEG file sizes with improved overall image quality.