Recent advances in cross-modal compression(CMC) have opened new horizons for perceptual image coding at ultra-low bitrates (below 0.1 bpp) within a generative compression paradigm, but reconstruction fidelity is often compromised, yielding visually plausible yet semantically inconsistent reconstructions. While prompt engineering with contextual optimization has been extensively explored in generative models, its potential for controlling perception-fidelity trade-offs in image compression remains largely under-explored. To address these challenges, we propose PO-CMC, a novel diffusion-based cross-modal image compression approach that introduces contextual prompt optimization to achieve efficient and perceptually faithful reconstruction. The proposed method comprises three synergistic components: an optimized image codec that produces a compact structural prior, a contextual prompt module that adaptively encodes semantic cues into compact textual embeddings, and a diffusion-based decoder that fuses the structural and semantic priors to reconstruct high-fidelity images. Extensive experiments show that PO-CMC achieves superior perceptual quality while maintaining comparable reconstruction fidelity, yielding an average BD-rate saving of 72.5 % and 79.8 % over VVC at equivalent LPIPS and DISTS levels, respectively.

For decades, the Bjøntegaard Delta (BD) has been the metric for evaluating codec Rate-Distortion (R-D) performance. Yet, in most studies, BD is determined using just 4–5 R-D data points, could this be sufficient? As codecs and quality metrics advance, does the conventional BD estimation still hold up? Crucially, are the performance improvements of new codecs and tools genuine, or merely artifacts of estimation flaws? We address these concerns by reevaluating BD estimation. We have established a large-scale, high-precision R-D dataset to verify the accuracy of existing BD estimation algorithms. Moreover, we propose a robust method for high-precision BD estimation across diverse compression scenarios, enhanced by a reliability assessment to determine the probability distribution of BD values from R-D sample points. This approach both assesses the reliability of BD calculations and serves as a precise BD estimator. Our method's validity is confirmed through extensive testing on a dataset we constructed. Our findings advocate for the adoption of rigorous R-D sampling and reliability metrics in future compression research to ensure the validity and reliability of results. Our code and additional experimental details are publicly accessible at https://github.com/fgvfgfg564/BDCI.