Tensor-Product B-Spline Predictor for Video Color Grade Transformation
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Solution Overview
Problem
Current methods for processing video content struggle to accurately transform between different dynamic ranges, such as standard dynamic range (SDR) and high dynamic range (HDR), leading to inaccuracies and reduced color precision, especially in vibrant colors, due to limitations in single-channel luminance and multi-channel chroma prediction techniques.
Innovation Solution
The implementation of tensor-product B-spline (TPB) predictors, which generate composer metadata to enable downstream processors to reconstruct images with improved picture quality and high color precision by modeling transformations between different color grades using continuous curves and minimizing differences between source and reference images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-channel luminance and multi-channel chroma prediction techniques are used, then the processing complexity is reduced, but the prediction accuracy and color precision deteriorate significantly
Solution Approach 1:
The invention segments the prediction process into separate luma and chroma prediction components, with the luma predictor handling luminance transformation and the chroma predictor handling chrominance transformation. This segmentation allows each component to be optimized independently, achieving high prediction accuracy without excessive overall complexity.
Solution Approach 2:
The invention transitions from traditional single-channel prediction to a multi-dimensional tensor-product B-spline approach that simultaneously processes luma and chroma channels with continuous curve modeling. This dimensional expansion enables capturing complex color grade transformations while maintaining computational efficiency through parameter sharing.
2Power
If traditional prediction methods are used, then the computational load is lower, but the color representation accuracy and artifact reduction deteriorate
Solution Approach 1:
The invention changes the parameter representation from discrete lookup table values to continuous B-spline curve parameters. This allows for smooth color grade transformations and eliminates the artifacts associated with discrete mappings, while the tensor-product structure efficiently manages the increased parameter space through shared control points.
Solution Approach 2:
The invention combines B-spline curve modeling with tensor-product mathematics to create a composite prediction framework. This composite approach integrates the smoothness properties of B-splines with the multi-dimensional transformation capabilities of tensor products, achieving superior color representation accuracy.
3Measurement precision
If continuous curve modeling is implemented, then the color precision and smoothness of transformation improve, but the computational complexity increases
Solution Approach 1:
The invention creates a universal tensor-product B-spline framework that handles both luma and chroma predictions through a unified mathematical structure. The same B-spline basis functions and control point mechanisms serve both color components, reducing redundant computation while maintaining high color precision.
Solution Approach 2:
The invention performs preliminary computation of B-spline basis functions and control points during the encoding phase, storing these pre-computed parameters in the bitstream. This preliminary action shifts computational burden from decoding to encoding, allowing smooth continuous transformations with reduced real-time computational complexity.
Data Source
AI summary
A set of tensor-product B-Spline (TPB) basis functions is determined. A set of selected TPB prediction parameters to be used with the set of TPB basis functions for generating predicted image data in mapped images from source image data in source images of a source color grade is generated. The set of selected TPB prediction parameters is generated by minimizing differences between the predicted image data in the mapped images and reference image data in reference images of a reference color grade. The reference images correspond to the source images and depict same visual content as depicted by the source images. The set of selected TPB prediction parameters is encoded in a video signal as a part of image metadata along with the source image data in the source images. The mapped images are caused to be reconstructed and rendered with a recipient device of the video signal.