ciede-gradient-nn

Training a neural network to approximate a novel color gradient function based on CIEDE2000.

Background

CIEDE2000 - a color difference formula based on human vision and used across industry.

Color gradient - an interpolation between two colors; a "path" from one color to another.

A novel gradient function has been developed based on CIEDE2000. It sometimes produces non-continuous gradients. Approximating it computationally requires a relatively long time.

This function was used to train a neural network, successfully reducing computation time while capturing its intricacies like non-continuity.

This repository contains the source code for the tools that were used to achieve this result:

• sample-generator - generates training and testing data for the neural network
• nn - creates, trains and tests a neural network
• image-generator - a tool for visualizing how the neural network compares to the original function

Building and Running

Requirements:

• Rust toolchain
• SDL2 developer libraries (for image-generator only)

cd sample-generator
cargo run --release

cd nn
cargo run --release

cd image-generator
cargo run --release

Note that sample-generator takes about 10 minutes to complete while using the default configuration.

sample-generator produces data-[seed].csv and nn produces nn.json.

All 3 tools have config.ini files that can be edited to change their configuration.

For convenience, nn uses provided sample_data.csv and image-generator uses provided sample_nn.json. This can be changed to user provided data.cvs and nn.json by setting use_sample_data and use_sample_nn to false in the respective configuration files.

Previews

Top rows - a slow computational approximation of the CIEDE2000 gradient between 2 random colors. Bottom rows - a fast neural network approximation of the same.

The neural network used for the generation of these images is contained in image-generator/sample_nn.json. It was trained with 10.9 million samples for 80 epochs.