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.

翻译：在有损图像压缩中，目标是在将图像压缩到特定位率的同时实现最小的信号畸变。随着视觉分析应用（尤其是分类任务）需求的日益增长，考虑压缩图像中的语义畸变变得尤为重要。为弥合图像压缩与视觉分析之间的差距，我们提出了一种用于有损图像压缩的率畸变分类（RDC）模型，该模型提供了一个统一框架来优化率、畸变与分类准确率之间的权衡。通过多分布源上的统计分析以及广泛使用的MNIST数据集上的实验，我们对RDC模型进行了全面评估。研究结果表明，在特定条件下，RDC模型展现出单调非减和凸函数等理想特性。本工作为发展人机友好型压缩方法及面向机器的视频编码（VCM）技术提供了见解，并为实际应用中的端到端图像压缩技术铺平了道路。