Enforcing a strict byte ceiling (such as $B_{target} = 1,048,576$ bytes) across diverse image content presents an algorithmic challenge. Because the entropy of an image varies dramatically based on spatial frequency, color variance, and edge complexity, a fixed quality factor (e.g., $Q = 80$) cannot reliably predict final file size. A flat landscape might compress to 450KB at $Q = 80$, whereas a dense foliage photograph or text-heavy document might output 1,800KB at the exact same setting. DeepSeek solves this through an iterative binary search over the quality parameter domain $Q \in [0.05, 0.98]$. Within 5 to 7 iterations, the engine locates the maximum quality factor $Q^*$ such that $\text{Size}(Q^*) \le 1,024\text{ KB}$, maximizing visual fidelity while strictly honoring the upper boundary.

Traditional compression benchmarks historically relied on Peak Signal-to-Noise Ratio (PSNR), which computes mean squared error between corresponding pixels. However, PSNR correlates poorly with human visual perception. DeepSeek measures visual fidelity using the Structural Similarity Index Measure (SSIM), which evaluates luminance, contrast, and structural correlation. By preserving structural relationships, our engine prevents the blurring of fine text and sharp edges even when high compression ratios are required to fit under the 1MB cap.