Neural Color Palette Prediction for Fast Image Quantization
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Solution Overview
Problem
Converting images between different color formats, especially for large images, is a slow process that can lead to a loss in perceptual quality and is computationally expensive, particularly when using methods like the median-cut algorithm.
Innovation Solution
Employing artificial neural networks (ANNs) and machine learning algorithms to generate color palettes that reduce the number of colors in images while maintaining subjective quality, using training methods that do not require ground truth palettes and utilizing convolutional neural networks (CNNs) for efficient color palette prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional color quantization methods (e.g., median-cut algorithm) are used to convert images to formats with fewer colors, then the number of colors is reduced, but the conversion process becomes slow and computationally expensive
Solution Approach 1:
The patent pre-calculates and stores color palettes for different image characteristics during an offline training phase. When conversion is needed, the system quickly retrieves the pre-computed palette instead of performing slow online quantization, thus maintaining fast conversion speed while achieving effective color reduction.
Solution Approach 2:
The patent replaces traditional iterative quantization algorithms (like median-cut) with a machine learning-based approach. A neural network is trained to predict optimal color palettes directly, substituting the mechanical iterative process with a learned model that provides faster and often more accurate results.
2Quantity of substance
If traditional color quantization methods are used to reduce the number of colors, then file size is reduced, but perceptual quality of the image deteriorates
Solution Approach 1:
The patent incorporates perceptual quality metrics into the training feedback loop. The neural network is trained using loss functions that measure perceptual differences between original and quantized images, allowing the model to learn palettes that minimize perceived quality loss rather than just numerical color differences.
Solution Approach 2:
The patent changes the optimization parameters from simple color distance metrics to perceptual quality metrics. By training the network to optimize for human perception rather than mathematical color space distances, the resulting palettes better preserve the visual appearance of images even with fewer colors.
Data Source
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AI summary
Example embodiments allow for training of encoders (e.g., artificial neural networks (ANNs)) to generate a color palette based on an input image. The color palette can then be used to generate, using the input image, a quantized, reduced color depth image that corresponds to the input image. Differences between a plurality of such input images and corresponding quantized images are used to train the encoder. Encoders trained in this manner are especially suited for generating color palettes used to convert images into different reduced color depth image file formats. Such an encoder also has benefits, with respect to memory use and computational time or cost, relative to the median-cut algorithm or other methods for producing reduced color depth color palettes for images.