Diamojism: semantic-perceptual tile selection for large-format emoji mosaic rendering

Abstract

Photomosaic systems have long treated the tile library as a purely visual dataset, selecting tiles by color and edge similarity alone. This paper presents a rendering architecture in which an emoji tile library is treated instead as a semantically annotated dataset, governed by a five-signal scoring model operating over an adaptively subdivided diamond tile grid. The architecture introduces a three-geometry artist region system, a two-pass scoring model, a semantic theme scoring layer that biases candidate selection toward emoji whose annotations match the source image’s theme vocabulary without overriding perceptual quality, and an iterative palette correction mechanism that corrects systematic candidate routing errors using render-history feedback. The architecture is demonstrated across five works whose differing painting techniques each motivate a distinct architectural response. Controlled comparisons from per-tile placement logs show that semantic theme scoring raises on-theme placement rate on each of the four works with theme presets configured, by 6.6–13.6 percentage points depending on the work’s palette and theme vocabulary, with no meaningful change in placement entropy. A separate comparison shows that palette correction reduces systematic lightness and chroma residuals by 40% and 69% on Birth of Emoji, one of two works where the correction was kept in production. The other three needed opposite-direction corrections across tile sizes that a single per-emoji adjustment cannot satisfy, so it was rolled back. Every example is a completed production work rather than a synthetic benchmark. This expands what photomosaic rendering can express without requiring generative models or post hoc style transfer.