CORD: Competitive Regional Diagrams for Fast and Compact Large Image Representation

Explicit primitive-based methods have achieved high fidelity but remain costly to scale to large images. Recent competitive representations improve reconstruction through per-pixel competition. We observe that the same geometry-dependent competition recurs throughout the pipeline: it is repeatedly updated over millions of pixels during fitting, altered by geometry quantization, and costly to rediscover during rendering. We introduce COmpetitive Regional Diagrams (CORD), which localizes this recurring competition within independently fitted regional diagrams that serve as common units of fitting, coding, and rendering. Bounded competitive fitting limits the size of each optimization problem and completes pruning early, leaving retained primitives time to adapt. Decoded-state coding adapts appearance to decoded geometry and evaluates alternatives by their decoded distortion and actual encoded size. Image-wide rate allocation reuses these alternatives across budgets and directs bytes by reconstruction gain, without per-budget retuning or retraining. Persistent contributor mapping compiles decoder-selected contributor lists into compact GPU state, sharing IDs across neighboring pixels to avoid repeated discovery at fixed geometry and output grid. Experiments show that CORD achieves better rate–distortion trade-offs than the evaluated baselines. On FGF2, CORD High gains 4.60 dB over SGI with 19% smaller payloads, an 18.9$\times$ optimization speedup, and nearly 200 FPS for repeated rendering on an A800 GPU.