HDR Reshaping Functions with Continuous Reversible Polynomial Segments
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Solution Overview
Problem
Existing image reshaping techniques for high-dynamic range (HDR) content are inefficient and computationally demanding, particularly in devices with limited processing power, necessitating improved methods for efficient coding and display of HDR content.
Innovation Solution
A method involving the construction of forward and backward reshaping functions using multi-segment polynomial representations with common pivot points, optimized to minimize gaps between segments and distance between original and reconstructed HDR values, enabling efficient coding and display of HDR content on both SDR and HDR devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional reshaping techniques are used for HDR content, then image quality can be maintained, but computational complexity and processing demands increase significantly
Solution Approach 1:
The patent divides the reshaping function into multiple polynomial segments with common pivot points. Each segment is optimized independently to minimize gaps while maintaining overall continuity. This segmentation allows for more efficient computation compared to traditional single-function approaches, directly addressing the contradiction between image quality and computational complexity.
Solution Approach 2:
The patent transforms the reshaping problem by changing parameters from continuous functions to discrete polynomial coefficients with shared pivot points. This parameter transformation enables more efficient storage and computation while maintaining the quality benefits of continuous functions, resolving the trade-off between precision and complexity.
2Measurement precision
If continuous reshaping functions are used to eliminate gaps between segments, then image quality improves, but reversibility and precision are compromised
Solution Approach 1:
By segmenting the reshaping function into multiple polynomial pieces with common pivot points, the patent achieves continuity (improving image quality) while maintaining exact representability at pivot points (preserving reversibility). The segmentation allows continuous optimization without sacrificing the mathematical properties needed for lossless reconstruction.
Solution Approach 2:
The patent employs an optimization process that uses feedback from both continuity constraints (minimizing gaps) and reversibility constraints (maintaining exact pivot point values). This dual-feedback mechanism ensures that image quality improves through continuity while reversibility is maintained through constrained optimization at critical points.
3Measurement precision
If complex optimization processes are applied to minimize gaps between polynomial segments, then image quality improves, but processing time and computational resources increase
Solution Approach 1:
The segmentation into polynomial pieces with common pivot points structure the optimization problem in a way that reduces computational burden. Instead of optimizing a single complex function, the patent optimizes multiple simpler polynomial segments with shared parameters, significantly reducing processing time while maintaining image quality.
Solution Approach 2:
The patent performs preliminary establishment of common pivot points across all segments before final optimization. This preliminary action structures the problem to enable more efficient subsequent optimization, reducing the overall computational time and resources needed while achieving the same image quality improvements.
Data Source
AI summary
Methods and systems for generating a set of forward and backward reshaping functions for the efficient coding of high-dynamic range (HDR) images are provided. Given an initial set of forward reshaping functions, output forward reshaping functions are constructed by a) using the forward reshaping functions to generate a first set of corresponding backward reshaping functions b) generating a second set of backward reshaping functions using a multi-segment polynomial representation with a common set of pivot points c) generating an output set of backward reshaping functions by optimizing the polynomial representation of the second set of backward reshaping functions to minimize gap values between consecutive segments and d) using the output set of backward reshaping functions to generate the output set of forward reshaping functions by minimizing the distance between original input HDR codewords and reconstructed HDR codewords.