Dynamic Joint Pixel Probability Generation: A Framework for Parallel Probabilistic Image Synthesis and Iterative Spatial Coordination
Abstract
Image generation can be formulated as the construction of a coherent image from probability distributions over possible pixel values. Although existing generative models learn image distributions, they differ substantially in how they represent uncertainty, coordinate spatial dependencies, and transform probabilistic predictions into final images. Autoregressive models factorize image likelihood into conditional pixel distributions, while diffusion models generate images through iterative denoising. These established approaches motivate the investigation of alternative mechanisms for organizing pixel-level probability estimation and image synthesis.
This paper proposes Dynamic Joint Pixel Probability Generation (DJPPG), a conceptual framework in which probability distributions are estimated for multiple pixel locations in parallel and subsequently coordinated through iterative spatial compatibility updates. Rather than requiring a fixed raster-order generation process, the proposed framework maintains distributions over candidate pixel values and updates these distributions using contextual information, neighboring pixel relationships, global image conditions, and uncertainty estimates. A joint energy function is introduced to encourage spatial consistency, while a probabilistic inference procedure converts the updated distributions into a final image.
The framework is formally specified through its probabilistic representation, compatibility functions, iterative update equations, training objectives, and inference procedure. A comparative experimental protocol is proposed to evaluate DJPPG against autoregressive, diffusion-based, and parallel pixel-synthesis baselines. No experimental results are claimed because the framework has not yet been implemented or empirically evaluated. The principal contribution is a testable formulation of iterative, jointly coordinated pixel probability generation. Its novelty and practical advantages remain to be established through implementation and systematic comparison with existing probabilistic image-generation methods.
Related
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BibTeX
@misc{zhang2026dynamic,
title = {{Dynamic Joint Pixel Probability Generation: A Framework for Parallel Probabilistic Image Synthesis and Iterative Spatial Coordination}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23615443},
url = {https://zenodo.org/records/23615443},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Dynamic Joint Pixel Probability Generation: A Framework for Parallel Probabilistic Image Synthesis and Iterative Spatial Coordination
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/24
PB - Zenodo
DO - 10.5281/zenodo.23615443
UR - https://zenodo.org/records/23615443
AB - Image generation can be formulated as the construction of a coherent image from probability distributions over possible pixel values. Although existing generative models learn image distributions, they differ substantially in how they represent uncertainty, coordinate spatial dependencies, and transform probabilistic predictions into final images. Autoregressive models factorize image likelihood into conditional pixel distributions, while diffusion models generate images through iterative denoising. These established approaches motivate the investigation of alternative mechanisms for organizing pixel-level probability estimation and image synthesis. This paper proposes Dynamic Joint Pixel Probability Generation (DJPPG), a conceptual framework in which probability distributions are estimated for multiple pixel locations in parallel and subsequently coordinated through iterative spatial compatibility updates. Rather than requiring a fixed raster-order generation process, the proposed framework maintains distributions over candidate pixel values and updates these distributions using contextual information, neighboring pixel relationships, global image conditions, and uncertainty estimates. A joint energy function is introduced to encourage spatial consistency, while a probabilistic inference procedure converts the updated distributions into a final image. The framework is formally specified through its probabilistic representation, compatibility functions, iterative update equations, training objectives, and inference procedure. A comparative experimental protocol is proposed to evaluate DJPPG against autoregressive, diffusion-based, and parallel pixel-synthesis baselines. No experimental results are claimed because the framework has not yet been implemented or empirically evaluated. The principal contribution is a testable formulation of iterative, jointly coordinated pixel probability generation. Its novelty and practical advantages remain to be established through implementation and systematic comparison with existing probabilistic image-generation methods.
ER -