Exemplar-Based Color Grading for Real-Time Live Content
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Solution Overview
Problem
Existing image processing systems struggle to efficiently and automatically color-grade live content with unpredictable conditions, such as live broadcasts and virtual reality, without manual intervention.
Innovation Solution
A method and system that utilizes exemplar-based color grading, where predetermined color-grading parameters from selected exemplar frames are applied to new content based on similarity measurements, enabling real-time or near real-time color grading by comparing new frames to manually color-graded exemplar frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual color grading is performed for each frame of live content, then color grading quality can be maintained, but processing time and productivity are severely reduced
Solution Approach 1:
The system performs preliminary color grading on selected exemplar frames before live content processing. These pre-graded exemplar frames serve as reference templates that capture the desired color grading style. When new content arrives, the system matches it against these pre-prepared exemplars and applies the corresponding grading parameters, eliminating the need for real-time manual grading of every frame while maintaining consistent quality.
2Adaptability or versatility
If color grading parameters are manually adjusted for each frame, then adaptability to dynamic conditions is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system performs self-service by automatically matching incoming content frames against the exemplar database and selecting appropriate color grading parameters without human intervention. The similarity comparison algorithm autonomously determines which exemplar best matches the current frame and applies the corresponding grading settings, enabling the system to adapt to dynamic live content conditions while minimizing operational complexity.
3Manufacturing precision
If multiple color-grading operations are applied to achieve high quality output, then manufacturing precision is improved, but processing time and loss of time increase
Solution Approach 1:
Multiple color-grading operations are pre-computed and stored as composite parameter sets in the exemplar database. Instead of applying multiple operations sequentially to each new frame, the system retrieves a single pre-computed parameter set that represents the combined effect of multiple grading operations, achieving high-quality output while dramatically reducing processing time.
4Productivity
If real-time color grading is implemented for live content, then productivity is improved, but manufacturing precision and color grading quality may deteriorate
Solution Approach 1:
The system creates copies of successfully graded exemplar frames and their associated parameter sets. When new content arrives, it finds the best matching exemplar copy and applies the copied grading parameters, ensuring that real-time processing maintains the same quality standards as carefully crafted manual grading while achieving high throughput.
Data Source
AI summary
Systems and methods for color grading of images and video based on similarity to exemplars. In preparation for color-grading new content, exemplar frames related to the expected new content may be obtained and color grading parameters for the exemplar frames may be obtained. To color grade the new content as it is created or received, similarities between frames of the new content and the exemplar frames may be determined. The similarities between frames of the new content and the exemplar frames may be determined may be combined with the obtained color-grading parameters from the exemplar frames to determine suitable color-grading parameters to apply to the new content. The new content may then be color-graded using the determined color-grading parameters.