Adaptive Piece-Wise Polynomial Mapping for Video Texture Consistency
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Solution Overview
Problem
Video codecs face issues with preserving texture/noise consistency in reconstructed images from multi-layer video signals, particularly due to content-mapping functions that cause object segmentation/fragmentation artifacts, leading to unnatural appearances in homogeneous regions.
Innovation Solution
The solution involves using a piece-wise function with polynomial pieces as predictors, where parameters are determined to minimize prediction errors between SDR and EDR images, and the last pivot point is adaptively adjusted based on residual energy peaks and noise levels to ensure consistent texture/noise in reconstructed images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content-mapping functions are adjusted to improve color mapping flexibility, then colorist control over content mapping is improved, but object segmentation artifacts increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting content-mapping function parameters (lifts, gains, gamma values) based on image characteristics. The system analyzes residual energy peaks and noise levels to adaptively modify mapping parameters, preventing object segmentation artifacts while preserving colorist control flexibility. This resolves the contradiction by making parameters variable rather than fixed, allowing optimization for different image content.
Solution Approach 2:
The patent implements feedback mechanisms by analyzing residual energy peaks and noise levels from the image data to adjust content-mapping parameters. The system uses this feedback to identify when homogeneous regions are being segmented and automatically modifies mapping parameters to preserve texture consistency. This feedback loop resolves the contradiction by continuously monitoring and correcting segmentation artifacts while maintaining mapping flexibility.
2Adaptability or versatility
If content-mapping functions are adjusted to improve color mapping flexibility, then colorist control over content mapping is improved, but texture consistency deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting content-mapping function parameters (lifts, gains, gamma values) based on image characteristics. The system analyzes residual energy peaks and noise levels to adaptively modify mapping parameters, preventing object segmentation artifacts while preserving colorist control flexibility. This resolves the contradiction by making parameters variable rather than fixed, allowing optimization for different image content.
Solution Approach 2:
The patent applies local quality by treating different regions of the image differently based on their characteristics. The system identifies homogeneous regions with consistent texture/noise patterns and applies specialized processing to these regions, while allowing more freedom in mapping for other regions. This localized approach preserves texture consistency where needed while maintaining overall mapping flexibility.
3Measurement precision
If piece-wise function with polynomial pieces is used to minimize prediction errors, then mapping precision is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the content-mapping function into multiple piece-wise polynomial segments. Each segment handles a specific range of input values with its own polynomial coefficients, allowing high precision mapping while keeping individual segments simple. This segmentation strategy resolves the contradiction by breaking down a complex mapping problem into multiple manageable parts.
Solution Approach 2:
The patent applies dynamics by making the mapping function adaptive rather than static. The piece-wise polynomial structure allows different mapping characteristics to be applied dynamically based on image content and residual energy analysis. This dynamic adaptability achieves high precision when needed while maintaining simplicity through automated parameter selection, resolving the complexity-precision tradeoff.
Data Source
Figure 1A~1B
Figure 2A
Figure 2B
AI summary
For each content-mapped frame of a scene, it is determined whether the content mapped frame is susceptible to object fragmentation with respect to texture in a homogeneous region based on statistical values derived from the content-mapped image and a source image mapped into the content-mapped image. The homogeneous region is a region of consistent texture in the source image. Based on a count of content-mapped frames susceptible to object fragmentation in homogeneous region, it is determined whether the scene is susceptible to object fragmentation in homogeneous region. If so, an upper limit for mapped codewords for a prediction function for predicting codewords of a predicted image from the mapped codewords in the content-mapped image is adjusted. Mapped codewords above the upper limit are clipped to the upper limit.