Adaptive Mosaic Element Generation: A Framework for Constructing Images Through Dynamic Spatial Organization of Visual Primitives

Abstract

Contemporary image generation methods commonly represent images as continuous pixel arrays, latent feature maps, or sequences of discrete visual tokens. This paper proposes Adaptive Mosaic Element Generation (AMEG), a framework that treats image synthesis as the progressive construction and organization of a variable collection of visual primitives. Inspired by the compositional structure of mosaic art, the framework represents an image as a set of elements with spatial positions, shapes, colors, scales, orientations, and optional appearance attributes. Rather than requiring the element configuration to remain fixed, AMEG permits elements to be created, relocated, recolored, split, merged, replaced, and removed during generation. A renderer maps the evolving element configuration into an image, while an evaluation mechanism estimates visual fidelity, structural coherence, perceptual quality, element complexity, and computational cost. The framework supports both reference-conditioned reconstruction and generative synthesis, with potential implementations based on differentiable optimization, learned policies, and reinforcement learning. A central design principle is adaptive spatial allocation: regions requiring greater representational detail may receive more elements, whereas relatively homogeneous regions may be represented by fewer, larger elements. This paper formalizes the framework, defines its optimization objectives, presents a proposed training procedure, and specifies an experimental protocol for evaluating its effectiveness. The contribution is a general research framework rather than a claim that mosaic rendering, adaptive tiling, or primitive-based image synthesis is new. Its principal research question is whether dynamic element-level structural operations can provide a useful and computationally controllable alternative to fixed-structure image representations.