arXiv subjects

Lalitha A R

Publications and source records attributed to Lalitha A R.

3 recordsLinked to original sources

arXiv2025

Context-Adaptive Color Optimization for Web Accessibility: Balancing Perceptual Fidelity and Functional Requirements

We extend our OKLCH-based accessibility optimization with context-adaptive constraint strategies that achieve near-universal success rates across diverse use cases. Our original strict algorithm reached 66-77% success by prioritizing minimal perceptual change ($ΔE \leq 5.0$), optimizing for enterprise contexts where brand fidelity is paramount. However, this one-size-fits-all approach fails to serve the broader ecosystem of web developers who need accessible solutions even when strict perceptual constraints cannot be satisfied. We introduce recursive optimization (Mode~1) that compounds small adjustments across iterations, achieving 93.68% success on all color pairs and 100% success on reasonable pairs (contrast ratio $ρ> 2.0$), representing a +27.23 percentage point improvement. A relaxed fallback mode (Mode~2) handles pathological edge cases, reaching 98.73% overall success. Evaluation on 10,000 realistic web color pairs demonstrates that context-aware constraint relaxation, combined with absolute hue preservation, enables practical accessibility compliance while maintaining brand color identity. The median perceptual change remains zero across all modes (most pairs already comply), while the 90th percentile reaches $ΔE_{2000} = 15.55$ in Mode~1 -- perceptually acceptable when hue invariance preserves the essential character of the original color. The approach is deployed in CM-Colors v0.5.0 (800+ monthly downloads), providing developers with explicit control over the accessibility-fidelity trade-off appropriate to their context.

cs.HC↗

arXiv2025

Perceptually-Minimal Color Optimization for Web Accessibility: A Multi-Phase Constrained Approach

Web accessibility guidelines require sufficient color contrast between text and backgrounds; yet, manually adjusting colors often necessitates significant visual deviation, compromising vital brand aesthetics. We present a novel, multi-phase optimization approach for automatically generating WCAG-compliant colors while minimizing perceptual change to original design choices. Our method treats this as a constrained, non-linear optimization problem, utilizing the modern perceptually uniform OKLCH color space. Crucially, the optimization is constrained to preserve the original hue ($\text{H}$) of the color, ensuring that modifications are strictly limited to necessary adjustments in lightness ($\text{L}$) and chroma ($\text{C}$). This is achieved through a three-phase sequence: binary search, gradient descent, and progressive constraint relaxation. Evaluation on a dataset of 10,000 procedurally generated color pairs demonstrates that the algorithm successfully resolves accessibility violations in $77.22\%$ of cases, with $88.51\%$ of successful corrections exhibiting imperceptible color difference ($ΔE_{2000} < 2.0$) as defined by standard perceptibility thresholds. The median perceptual change for successful adjustments is only $0.76\ ΔE_{2000}$, and the algorithm achieves this with a median processing time of $0.876\text{ms}$ per color pair. The approach demonstrates that accessibility compliance and visual design integrity can be achieved simultaneously through a computationally efficient, perceptually-aware optimization that respects brand identity. The algorithm is publicly implemented in the open-source cm-colors Python library.

cs.HC↗

arXiv2025

Beyond Compliance: A User-Autonomy Framework for Inclusive and Customizable Web Accessibility

This paper proposes a shift from compliance-centered web accessibility to a care-driven model that prioritizes user autonomy, using neurodivergent users as a catalyst case for broader personalization needs. While accessibility standards offer a flexible framework, they are often interpreted and implemented as static compliance checklists, our approach reframes it as a flexible, user-centered process. We introduce a customizable Comfort Mode framework that allows users to adapt interface settings, such as contrast, typography, motion, and scaling, according to their individual needs, while retaining the brand's core visual identity. Grounded in psychological and cognitive accessibility principles, our design supports personalization without sacrificing creative freedom. We present both minimal and advanced implementation models with mock-ups, demonstrating how inclusive design can be seamlessly integrated at minimal cost. This approach aims to broaden digital inclusivity by offering autonomy to those who require it, without imposing changes on those who do not. The proposed system is adaptable, scalable, and suitable for a wide range of users and brands, offering a new paradigm where user autonomy, aesthetic integrity, and accessibility converge not through compromise, but through choice.

cs.HC↗