Optimizing Data Efficiency and Visual Quality with Intelligent Thresholding in Color Encoding

Effectively balancing visual representation with data reduction is a central challenge in color reduction encoding. While recent advancements have improved visual quality, the key question is how much information can be safely removed without degrading the image. The proposed smart thresholding method addresses this challenge by using an L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>-informationbased approach of optimal data retention. By setting a noise limit threshold, essential image details are preserved while eliminating redundant data. Through the application of L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>-norm PCA, the algorithm identifies the point where further color reduction significantly impacts quality, offering an optimal balance between computational efficiency and performance. This approach is crucial for achieving target quantization levels without sacrificing vital image features.