Improving Variable-Rate Learned Image Compression with Transformer-Based QR Prediction and Perceptual Optimization

We present a variable-rate learned image compression (LIC) model that integrates Transformer-based quantization–reconstruction (QR) offset prediction, entropy-guided hyper-latent quantization, and perceptually informed multi-objective optimization. Unlike existing LIC frameworks that train separate networks for each bitrate, the proposed method achieves continuous rate adaptation within a single model by dynamically balancing rate, distortion and perceptual objectives. Channel-wise asymmetric quantization and a composite loss combining MSE and LPIPS further enhance reconstruction fidelity and subjective quality. Experiments on the Kodak, CLIC2020 and Tecnick datasets show gains of +1.15 dB PSNR, +0.065 MS-SSIM, and −0.32 LPIPS relative to the baselines variable-rate method, while improving bitrate-control accuracy by 62.5%. With approximately 15% computational overhead, the framework achieves competitive compression efficiency and enhanced perceptual quality, offering a practical solution for adaptive, high-quality image delivery.