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A Rate-Distortion-Classification Approach for Lossy Image Compression

Yuefeng Zhang

TL;DR

This work proposes Rate-Distortion-Classification (RDC), a joint optimization framework that extends rate-distortion theory to account for machine-based visual analysis by incorporating a classification constraint. The authors develop theoretical RDC formulations, analyze Bernoulli and general source cases, and demonstrate convexity and monotonicity properties under suitable assumptions. Empirically, they validate RDC on MNIST using an auto-encoder with interval coding and soft quantization, balancing distortion and a pretrained classifier’s performance to observe the predicted rate–distortion–classification trade-offs. The results indicate that higher coding rates reduce both pixel-level distortion and classification error, supporting the viability of end-to-end, machine-friendly compression approaches and guiding future Video Coding for Machine (VCM) methods.

Abstract

In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. The increasing demand for visual analysis applications, particularly in classification tasks, has emphasized the significance of considering semantic distortion in compressed images. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classification (RDC) model for lossy image compression, offering a unified framework to optimize the trade-off between rate, distortion, and classification accuracy. The RDC model is extensively analyzed both statistically on a multi-distribution source and experimentally on the widely used MNIST dataset. The findings reveal that the RDC model exhibits desirable properties, including monotonic non-increasing and convex functions, under certain conditions. This work provides insights into the development of human-machine friendly compression methods and Video Coding for Machine (VCM) approaches, paving the way for end-to-end image compression techniques in real-world applications.

A Rate-Distortion-Classification Approach for Lossy Image Compression

TL;DR

This work proposes Rate-Distortion-Classification (RDC), a joint optimization framework that extends rate-distortion theory to account for machine-based visual analysis by incorporating a classification constraint. The authors develop theoretical RDC formulations, analyze Bernoulli and general source cases, and demonstrate convexity and monotonicity properties under suitable assumptions. Empirically, they validate RDC on MNIST using an auto-encoder with interval coding and soft quantization, balancing distortion and a pretrained classifier’s performance to observe the predicted rate–distortion–classification trade-offs. The results indicate that higher coding rates reduce both pixel-level distortion and classification error, supporting the viability of end-to-end, machine-friendly compression approaches and guiding future Video Coding for Machine (VCM) methods.

Abstract

In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. The increasing demand for visual analysis applications, particularly in classification tasks, has emphasized the significance of considering semantic distortion in compressed images. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classification (RDC) model for lossy image compression, offering a unified framework to optimize the trade-off between rate, distortion, and classification accuracy. The RDC model is extensively analyzed both statistically on a multi-distribution source and experimentally on the widely used MNIST dataset. The findings reveal that the RDC model exhibits desirable properties, including monotonic non-increasing and convex functions, under certain conditions. This work provides insights into the development of human-machine friendly compression methods and Video Coding for Machine (VCM) approaches, paving the way for end-to-end image compression techniques in real-world applications.
Paper Structure (18 sections, 22 equations, 3 figures, 2 tables)

This paper contains 18 sections, 22 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Distortion-Perception
  4. Rate-Distortion-Perception
  5. Classification-Distortion-Perception
  6. RDC and Multi-distribution Source Analysis
  7. RDC Joint Optimization Modeling
  8. Bernoulli Source
  9. General Source
  10. Experiments
  11. Experiment Settings
  12. Model Settings
  13. Dataset
  14. Training Settings
  15. Result Analysis
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Training loss diagram with $L=3,\lambda=0.015$. Left: Distortion loss. Right: Classification loss.
  • Figure 2: Reconstructed images from the compression model with different $\lambda$.
  • Figure 3: (a) RDC two-dimensional diagram. (b) RDC three-dimensional diagram.