Neural Network Encoding for 3D Color Lookup Tables
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Solution Overview
Problem
Existing color manipulation applications face challenges in efficiently storing and processing large quantities of color lookup tables (LUTs) due to memory storage constraints.
Innovation Solution
Encoding a plurality of LUTs into a machine learning model that reduces the memory resource footprint, provides combinations of LUTs, and generates invertible approximations of LUTs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large quantity of LUTs is stored in memory, then color manipulation versatility is improved, but memory storage requirements increase
Solution Approach 1:
The patent combines multiple LUTs into a single neural network model that can dynamically generate different LUTs based on input parameters. Instead of storing hundreds of separate LUT files in memory, the system merges them into one compact neural network that reconstructs LUTs on-demand, dramatically reducing memory footprint while maintaining the ability to access diverse color manipulation functions.
Solution Approach 2:
The neural network model serves as a universal container that can generate multiple different LUTs through a single model instance. By using input identifiers and conditional logic within the neural network, the system achieves multi-functionality where one model replaces many specialized LUT files, allowing the same hardware resources to support diverse color manipulation tasks.
2Quantity of substance
If LUTs are compressed using traditional data compression, then memory footprint is reduced, but compression ratios are limited
Solution Approach 1:
The patent replaces traditional mechanical data compression algorithms with a neural network-based reconstruction system. Instead of applying compression algorithms to LUT data, the system trains a neural network to learn the underlying patterns and relationships in LUT data, then uses this learned model to reconstruct LUTs with much higher efficiency and lower memory requirements than conventional compression methods.
Solution Approach 2:
The system changes the fundamental parameter representation from storing complete LUT data to storing neural network weights and parameters. This parameter transformation allows the system to represent complex LUT information in a compressed form that can be efficiently stored and quickly reconstructed, achieving superior compression ratios compared to traditional methods.
3Measurement precision
If high resolution LUTs are used, then color accuracy is improved, but memory storage requirements increase
Solution Approach 1:
The neural network model stores only the essential parameters and patterns needed to reconstruct high-resolution LUTs, rather than storing complete high-resolution data. During reconstruction, the model generates the full high-resolution output on-demand, achieving accurate color representation with minimal stored data by performing partial storage with complete reconstruction.
Data Source
AI summary
The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for encoding color lookup tables (LUTs) by an apparatus. A method includes determining a first identifier corresponding to a first LUT of a plurality of LUTs, providing, to a trained machine learning model, the first identifier and an input lattice, obtaining, from the trained machine learning model, an output lattice corresponding to the first LUT, and performing color manipulation on at least one input image using the output lattice. Each LUT of the plurality of LUTs includes mappings from input color values to output color values. The trained machine learning model has been jointly trained on the plurality of LUTs and identification information corresponding to each LUT in the plurality of LUTs.