Modeling Image Quantization Tradeoffs for Optimal Compression

TL;DR

This paper introduces a deep learning-based method to optimize quantization tables for lossy image compression, improving the tradeoff between compression rate and image quality.

Contribution

It presents a novel CNN approach that learns to optimize quantization tables using a minimax loss, enhancing rate-distortion tradeoff measurement over prior methods.

Findings

Improved compression quality with optimized quantization tables.

Effective unsupervised learning of quantization mappings.

Potential applicability to various lossy compression algorithms.

Abstract

All Lossy compression algorithms employ similar compression schemes -- frequency domain transform followed by quantization and lossless encoding schemes. They target tradeoffs by quantizating high frequency data to increase compression rates which come at the cost of higher image distortion. We propose a new method of optimizing quantization tables using Deep Learning and a minimax loss function that more accurately measures the tradeoffs between rate and distortion parameters (RD) than previous methods. We design a convolutional neural network (CNN) that learns a mapping between image blocks and quantization tables in an unsupervised manner. By processing images across all channels at once, we can achieve stronger performance by also measuring tradeoffs in information loss between different channels. We initially target optimization on JPEG images but feel that this can be expanded to any lossy compressor.