  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.
