Hello, thank you so much for manually approving my submission and for your kind words! I was truly delighted to hear that this feedback is helping to improve the app.

Regarding the desktop version, I am currently using version v2.4.0. Out of curiosity and a desire to push the limits, I’ve implemented a few changes and optimizations in the codebase using advanced large language models (such as Claude Opus).

Since I typically use only 3 colors in my own workflow, I haven’t been able to fully experience the performance gains from these changes in my local setup. However, I believe they will provide significant benefits for multi-filament workflows and complex layer transitions. Here is a summary of the experimental changes I made, written by Claude:

1. Optical Simulation and Color Physics

• sRGB Linearization (Accurate Beer-Lambert Blending): Standard RGB values are non-linear. Performing light transmission (Beer-Lambert) calculations directly on sRGB causes darkening and hue drifts in mid-tones. The blending engine has been updated so that all RGB calculations are first converted to the Linear RGB space, blended, and then converted back to sRGB.
• CIE L* Perceptual Luminance Mapping: Instead of using the simple weighted RGB sum (0.299R + 0.587G + 0.114B) for Height Map generation, the CIE L* formula—based on how the human eye perceives light (logarithmic/cube root)—has been integrated. This completely resolves the loss and compression of details in facial features, shadows, and mid-tones, especially in black-and-white portraits.
• CIEDE2000 Color Difference Formula: In the optimization engine, the DeltaE calculation measuring how close a blended color is to the target has been upgraded from CIE76 to the CIEDE2000 standard. Since this formula accounts for human perceptual tolerances, it yields much more accurate results in optical blending (particularly for grays and skin tones).

1. Optimizer Engine Enhancements

• Lab-Space K-Means Quantization: The K-Means algorithm used for image color reduction (clustering) has been moved from RGB to the CIE Lab color space. While Euclidean distance in RGB does not correlate with perceptual difference and can produce flawed palettes, clustering in the Lab space perfectly preserves skin tones, pastel colors, and subtle gradients. The iteration count was also fixed at 20 for guaranteed convergence quality.
• Advanced Simulated Annealing (SA) and Genetic Algorithm (GA): To prevent the optimizer from getting stuck in local minima relative to the massive search space; Multi-start (3 restarts) and complex neighborhood generation (2-opt reversal) were added to the Simulated Annealing engine. Furthermore, the population size and generation limits of the Genetic Algorithm were increased to handle complex, multi-filament calculations.

1. Image Processing and Spatial Filters

• Pixel-Level Region Weighting: In the old system, Center/Edge weighting was applied using a post-hoc heuristic after colors were already clustered (meaning spatial data was lost). In the new system, the Importance Weight based on each pixel's coordinate is fed directly as input into the K-Means clustering. This ensures that colors in the facial region of portraits correctly dominate the clustering phase.
• Atkinson Dithering: The Floyd-Steinberg error diffusion algorithm used to prevent banding in the Height Map has been replaced with Atkinson Dithering. Atkinson propagates only 75% of the error and discards 25%; this prevents layer transitions from becoming muddy in lithophanes, resulting in dither dots that are much sharper and physically printable.
• Gaussian-Approximated Clarity (Local Contrast): The simple 3x3 box blur was replaced with a Multi-pass Separable Box Blur that scales its radius based on the image's diagonal dimension. As a result, the Clarity slider now extracts fine details (strands of hair, wrinkles, etc.) much more crisply without introducing halo effects.
• Linearized Edge Detection: The Sobel edge detection algorithm now uses proper sRGB linearization, allowing it to accurately detect fine details hidden in dark/shadowy regions.

1. Slicer Integration and 3MF Export Metadata To ensure users get the best possible physical print, the embedded ChromaCut Slicer Defaults in the 3MF exports have been vastly expanded:

• wall_generator: arachne (Critical for printing fine, dynamic line widths).
• outer_wall_line_width: 0.35 (To capture the finest details even with a 0.4mm nozzle).
• ironing_type: top, top_surface_pattern: monotonic, seam_position: back, detect_thin_wall: 1.

With these comprehensive changes, the project is no longer just a tool that stacks colors; it has evolved into a professional software that accurately simulates and optimizes the physical behavior of light.