JQF: Optimal JPEG Quantization Table Fusion by Simulated Annealing on Texture Images and Predicting Textures

TL;DR

This paper introduces JQF, a method that uses simulated annealing on texture mosaic images and CNN-based texture prediction to optimize JPEG quantization tables, achieving significant size reduction with minimal quality loss.

Contribution

It presents a novel approach combining texture mosaic images and CNN features to optimize JPEG quantization tables efficiently for each texture category.

Findings

23.5% size reduction over standard JPEG tables

0.35% FSIM decrease, visually imperceptible

Less than one second additional encoding time

Abstract

JPEG has been a widely used lossy image compression codec for nearly three decades. The JPEG standard allows to use customized quantization table; however, it's still a challenging problem to find an optimal quantization table within acceptable computational cost. This work tries to solve the dilemma of balancing between computational cost and image specific optimality by introducing a new concept of texture mosaic images. Instead of optimizing a single image or a collection of representative images, the simulated annealing technique is applied to texture mosaic images to search for an optimal quantization table for each texture category. We use pre-trained VGG-16 CNN model to learn those texture features and predict the new image's texture distribution, then fuse optimal texture tables to come out with an image specific optimal quantization table. On the Kodak dataset with the quality setting $Q=95$, our experiment shows a size reduction of 23.5% over the JPEG standard table with a slightly 0.35% FSIM decrease, which is visually unperceivable. The proposed JQF method achieves per image optimality for JPEG encoding with less than one second additional timing cost. The online demo is available at https://matthorn.s3.amazonaws.com/JQF/qtbl_vis.html