HDR Video Lookup Table Construction with Slope-Based Node Refinement
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Solution Overview
Problem
Existing methods for generating lookup tables for video prediction in high dynamic range (HDR) environments face challenges due to high computational complexity and limited memory, particularly in consumer devices, leading to significant interpolation errors.
Innovation Solution
A low-complexity method for constructing 3D lookup tables involves determining a set of nodes using parametric equations and refining them based on slope modifications between nodes, reducing interpolation errors while adhering to hardware constraints such as memory and computational power limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lookup table construction methods are used for HDR video prediction, then computational accuracy is improved, but device complexity and memory requirements increase significantly
Solution Approach 1:
The patent modifies the node determination process by using slope-based adjustments to traditional parametric equations. Instead of directly computing nodes from standard formulas, the method calculates slopes between adjacent nodes and uses these slopes to refine node positions, thereby reducing interpolation errors while maintaining computational efficiency suitable for consumer devices.
Solution Approach 2:
The lookup table construction is divided into distinct stages: initial node determination using parametric equations, slope calculation between nodes, and iterative node refinement based on slope adjustments. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while improving accuracy.
2Measurement precision
If traditional lookup table construction methods are used for HDR video prediction, then computational accuracy is improved, but memory requirements increase significantly
Solution Approach 1:
By changing the node determination parameters from direct parametric computation to slope-based iterative refinement, the patent reduces the number of nodes required in the lookup table. This approach achieves reduced interpolation error with fewer stored values, thereby decreasing memory requirements for consumer devices.
3Quantity of substance
If lookup table size is reduced to meet memory constraints, then device complexity is reduced, but interpolation error increases
Solution Approach 1:
The patent uses slope-based node refinement to optimize the distribution and positioning of lookup table nodes. By adjusting node positions based on calculated slopes rather than uniform spacing, the method achieves higher interpolation accuracy with fewer nodes, thereby reducing memory requirements without sacrificing precision.
Solution Approach 2:
The method performs preliminary slope calculations between nodes before finalizing the lookup table construction. This preliminary action allows for optimal node positioning to be determined in advance, ensuring that the reduced-size lookup table achieves maximum interpolation accuracy with minimal memory usage.
Data Source
AI summary
In a method to reconstruct a high dynamic range video signal, a decoder receives parameters in the input bitstream to generate a prediction function. Using the prediction function, it generates a first set of nodes for a first prediction lookup table, wherein each node is characterized by an input node value and an output node value.Then, it modifies the output node values of one or more of the first set of nodes to generate a second set of nodes for a second prediction lookup table, and generates output prediction values using the second lookup table. Low-complexity methods to modify the output node value of a current node in the first set of nodes based on computing modified slopes between the current node and nodes surrounding the current node are presented.