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.
Abstract (translated)
在损失图像压缩中，目标是实现压缩图像到指定比特率的同时最小化信号畸变。随着视觉分析应用的需求，特别是在分类任务中，考虑语义畸变在压缩图像中具有重要性。为了在图像压缩和视觉分析之间弥合差距，我们提出了一个名为 Rate-Distortion-Classification (RDC) 的模型，为损失图像压缩提供了一个统一的框架，以优化在速率、畸变和分类精度之间的平衡。RDC模型在多分布源上进行了广泛分析，同时在广泛使用的MNIST数据集上进行了实验验证。研究结果表明，在某些条件下，RDC模型表现出良好的特性，包括单调非递增和凸函数。这项工作揭示了人机友好压缩方法和视频编码机器（VCM）途径的发展，为现实应用中的端到端图像压缩技术铺平了道路。
URL
https://arxiv.org/abs/2405.03500
