Abstract: Background　Current medical image compression techniques primarily optimize for mean squared error (MSE), which does not fully capture human subjective perception of image quality and often fail to preserve the structural features essential for clinical diagnosis.Objective　To propose a low-loss compression coding algorithm for subtle features in medical images, aiming to reduce transmission bandwidth without compromising subjective image quality. Methods　CT image sequences from 14 orthopedic surgeries at the Chinese PLA General Hospital were collected in this study. Firstly, the Structural Similarity Index (SSIM) was reconstructed based on key visual features of medical images, including brightness, contrast, and detail texture, with the brightness factor set to α = 1.15 and the contrast/structure factors set to β = γ = 0.95. Subsequently, a relationship between the Structural Similarity Index and the Mean Squared Error (MSE) was established based on the linear distortion model and the law of large numbers. Then, 1/SSIM was employed as a distortion metric, and an SSIM-based distortion measure suitable for rate-distortion optimization (RDO) was constructed. On this basis, an SSIM-based rate-distortion optimization framework was developed by minimizing the distortion metric under a given target bitrate constraint. Finally, the proposed method was implemented on the x264 platform, and its rate-distortion performance was compared with that of the standard encoder to verify its advantages. Results　 Compared to the standard x264 encoder, our approach achieved an average rate-distortion gain of −5.2% under constant quantization parameter and −4.8% under constant quality factor. In terms of subjective quality, the SSIM of the encoded images remained above 0.95, with an average bitrate reduction of 372 kbps. Furthermore, no increase in computational complexity or encoding time was observed.Conclusion　The proposed method effectively preserves the high perceptual quality of medical images while maintaining computational efficiency, offering a superior compression solution for medical image transmission.