Adaptive Multiscale Compositional Image Generation: A Framework for Joint Global Structure and Independent Local Image Synthesis
Abstract
Conventional image generation methods primarily aim to produce a coherent image whose local details support its global semantic content. However, a different generation objective is to construct a single image that supports two complementary modes of visual interpretation: at a distance, the image presents a recognizable global pattern, while at close range, it consists of numerous independently meaningful images. Although photomosaics, hierarchical image synthesis, multiscale perceptual optimization, and tiled diffusion have addressed related objectives, opportunities remain to investigate adaptive coordination between global composition and independently generated local image units.
This paper proposes Adaptive Multiscale Compositional Image Generation (AMCG), a framework for coordinating global image structure and local semantic content through a shared multiscale representation. The framework consists of global composition modeling, local image-unit generation, spatial assignment, cross-scale evaluation, and iterative refinement. Unlike a strictly fixed-grid formulation, AMCG allows the number, positions, dimensions, and content of local image units to vary, subject to computational and compositional constraints. A multiscale objective combines global semantic fidelity, local image recognizability, cross-scale consistency, content diversity, layout quality, and computational cost. A proposed reinforcement-learning extension treats editing operations as sequential decisions guided by scale-dependent rewards. The paper formalizes the framework, describes an implementable algorithm, establishes an evaluation protocol, and identifies relevant baselines and ablation studies. Because the proposed framework has not yet been empirically evaluated in this manuscript, no performance improvement is claimed. The primary research question is whether adaptive, bidirectional coordination can improve the balance between global recognizability and local semantic independence relative to existing photomosaic and tiled-generation methods.
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BibTeX
@misc{zhang2026adaptive,
title = {{Adaptive Multiscale Compositional Image Generation: A Framework for Joint Global Structure and Independent Local Image Synthesis}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23556115},
url = {https://zenodo.org/records/23556115},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Adaptive Multiscale Compositional Image Generation: A Framework for Joint Global Structure and Independent Local Image Synthesis
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/22
PB - Zenodo
DO - 10.5281/zenodo.23556115
UR - https://zenodo.org/records/23556115
AB - Conventional image generation methods primarily aim to produce a coherent image whose local details support its global semantic content. However, a different generation objective is to construct a single image that supports two complementary modes of visual interpretation: at a distance, the image presents a recognizable global pattern, while at close range, it consists of numerous independently meaningful images. Although photomosaics, hierarchical image synthesis, multiscale perceptual optimization, and tiled diffusion have addressed related objectives, opportunities remain to investigate adaptive coordination between global composition and independently generated local image units. This paper proposes Adaptive Multiscale Compositional Image Generation (AMCG), a framework for coordinating global image structure and local semantic content through a shared multiscale representation. The framework consists of global composition modeling, local image-unit generation, spatial assignment, cross-scale evaluation, and iterative refinement. Unlike a strictly fixed-grid formulation, AMCG allows the number, positions, dimensions, and content of local image units to vary, subject to computational and compositional constraints. A multiscale objective combines global semantic fidelity, local image recognizability, cross-scale consistency, content diversity, layout quality, and computational cost. A proposed reinforcement-learning extension treats editing operations as sequential decisions guided by scale-dependent rewards. The paper formalizes the framework, describes an implementable algorithm, establishes an evaluation protocol, and identifies relevant baselines and ablation studies. Because the proposed framework has not yet been empirically evaluated in this manuscript, no performance improvement is claimed. The primary research question is whether adaptive, bidirectional coordination can improve the balance between global recognizability and local semantic independence relative to existing photomosaic and tiled-generation methods.
ER -