Image Quantization Using Machine Learning Models

The patent applies universality by training a single machine learning model to handle multiple images simultaneously, learning a consistent color mapping that works across the entire dataset. The model learns universal color relationships rather than image-specific quantization, enabling consistent color palettes across all input images while maintaining visual similarity for downstream computer vision tasks.

The patent employs preliminary action by pre-training the machine learning model on a large dataset before applying quantization. The model learns optimal color mappings in advance through training on reference images and target images, so that when actual quantization is performed, the consistent color relationships are already established, ensuring both reduction in color quantity and maintenance of consistency across images.

The patent implements feedback by using a loss function that measures the difference between original images and quantized images, then backpropagating this error to update the model parameters. This continuous feedback loop ensures that the quantization process maintains visual similarity by adjusting the color mappings to minimize information loss while still reducing the number of distinct colors.

The patent replaces traditional mechanical quantization methods (such as k-means clustering or palette quantization) with a machine learning-based approach. Instead of using fixed algorithms that blindly reduce colors, the neural network learns optimal color mappings that preserve visual information, substituting the mechanical reduction process with an intelligent system that understands image content and maintains visual similarity.

The patent merges the processing of multiple images into a single unified model training process. Instead of optimizing each image separately, the model learns from batches of images simultaneously, combining the quantization task across the entire dataset. This merging approach maintains image quality through learned color mappings while dramatically improving processing efficiency by handling multiple images in parallel during training and inference.

The patent performs preliminary training on a large dataset to establish consistent color mappings that work across all images. This pre-learning phase captures general color relationships and patterns, so that when actual quantization is applied to individual images or new datasets, the model can quickly produce high-quality results without requiring separate optimization for each image, thus maintaining both quality and speed.