Algorithmization of the Creative Process in Commercial Photography: Methodology of Hybrid Visualization

Citation: Anastasiia Tamarina, "Algorithmization of the Creative Process in Commercial Photography: Methodology of Hybrid Visualization", Universal Library of Multidisciplinary, Volume 03, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This monograph presents a comprehensive study of the methodology for algorithmizing the creative process in commercial photography, based on the concept of hybrid visualization, which combines neural network computational capabilities with the physicochemical parameters of analog imagery. The work systematizes the theoretical foundations of the aesthetic crisis of digital sterility, reveals the psychophysiological mechanisms of texture and color perception, and substantiates the economic necessity of transitioning from manual post-production to algorithmically controlled processing pipelines amid growing data volumes and professional burnout. The monograph describes the technological infrastructure for training neural network profiles on paired digital RAW and film-scan datasets, where digitized film serves as a reference base for calibrating nonlinear tone mapping, color transformations, and grain modeling as a parameterized stochastic process. An engineering scheme is presented for integrating the hybrid algorithm into the production cycle, from the distribution of roles between film and digital on set to a standardized AI culling pipeline, batch neural network processing, and final human artistic validation, with a quantitative justification of efficiency via a TDABC model and calculations of labor reduction and cost savings while maintaining a premium Film Look.

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https://doi.org/10.70315/uloap.ulmdi.2026.0301006