Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

TL;DR

This paper introduces novel color processing and visual illusion-based algorithms to improve the legibility of papyri texts, emphasizing personalization and critical visual analysis for scholarly editing.

Contribution

It develops and compares new image enhancement algorithms tailored for critical visual tasks, highlighting the importance of user personalization and contextual factors.

Findings

Proposed methods outperform classic enhancement techniques.

User behavior varies widely based on personality, expertise, and context.

A framework for critical image enhancement considering uncertainty and interpretation.

Abstract

Purpose: This article develops theoretical, algorithmic, perceptual, and interaction aspects of script legibility enhancement in the visible light spectrum for the purpose of scholarly editing of papyri texts. - Methods: Novel legibility enhancement algorithms based on color processing and visual illusions are compared to classic methods in a user experience experiment. - Results: (1) The proposed methods outperformed the comparison methods. (2) Users exhibited a broad behavioral spectrum, under the influence of factors such as personality and social conditioning, tasks and application domains, expertise level and image quality, and affordances of software, hardware, and interfaces. No single enhancement method satisfied all factor configurations. Therefore, it is suggested to offer users a broad choice of methods to facilitate personalization, contextualization, and complementarity. (3) A distinction is made between casual and critical vision on the basis of signal ambiguity and error consequences. The criteria of a paradigm for enhancing images for critical applications comprise: interpreting images skeptically; approaching enhancement as a system problem; considering all image structures as potential information; and making uncertainty and alternative interpretations explicit, both visually and numerically.