Automated Video Color Segmentation Using Gamma Correction
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Solution Overview
Problem
Current visual effects (VFX) tasks in film and television, particularly green screen removal and segmentation, are labor-intensive and costly, despite advances in AI and computer vision, and struggle with low light and low contrast content, requiring significant user interaction and time.
Innovation Solution
An automated system using Gaussian Mixture Models and AI/ML approaches for color segmentation, which includes user identification of a color band, automatic gamma correction, and color space transformation to efficiently segment green or blue screens in videos, reducing user interaction and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional labor-intensive methods are used for green screen removal and segmentation, then user control and precision can be maintained, but processing time and cost increase significantly
Solution Approach 1:
The system performs preliminary color space transformation and gamma correction on the video content before segmentation. By pre-processing the video to transform colors to a perceptually uniform space and correct gamma encoding, the segmentation algorithm can more easily identify chroma key regions, reducing the need for iterative manual adjustments and speeding up the overall process while maintaining precision.
Solution Approach 2:
The patent introduces an intermediary color space transformation layer between the original video content and the segmentation process. By converting video colors to a perceptually uniform color space and applying gamma correction as intermediate steps, the system creates a more favorable processing environment for automated segmentation, reducing both time and manual intervention while preserving segmentation accuracy.
2Productivity
If automated AI/ML methods are used for color segmentation, then processing speed increases, but performance degrades in low light and low contrast scenarios
Solution Approach 1:
The system dynamically adjusts processing parameters based on scene characteristics. By detecting low light and low contrast conditions, the system modifies gamma correction parameters and color space transformation settings to enhance the visibility of chroma key regions in difficult lighting conditions, allowing automated methods to maintain reliability while preserving processing speed advantages.
Solution Approach 2:
The patent applies preliminary gamma correction and color space transformation to enhance contrast and brightness information before automated segmentation. This pre-processing step compensates for poor lighting conditions by normalizing the perceptual representation of colors, enabling AI/ML algorithms to reliably identify green or blue screen regions even in low light scenarios without sacrificing processing speed.
3Measurement precision
If manual user interaction is required for video segmentation, then segmentation accuracy can be maintained, but user effort and time consumption increase
Solution Approach 1:
The system performs self-service through automated color space transformation and gamma correction that prepares the video content for segmentation without requiring manual user intervention. The automated preprocessing steps independently enhance the segmentation input quality, reducing the need for user effort in parameter tuning and region selection while maintaining high segmentation accuracy through algorithmic optimization.
Solution Approach 2:
By performing preliminary automated color space transformation and gamma correction, the system prepares optimal segmentation conditions without user involvement. This automated pre-processing establishes accurate color boundaries and contrast levels that would otherwise require manual adjustment, thereby maintaining segmentation accuracy while eliminating user effort and time consumption.
4Extent of automation
If complex AI/ML models are used for segmentation, then automation level increases, but computational resources and processing complexity increase
Solution Approach 1:
The patent segments the processing pipeline into distinct stages: color space transformation, gamma correction, and segmentation. By dividing the complex AI/ML process into modular preprocessing steps followed by a simpler segmentation algorithm, the system achieves high automation levels while reducing computational complexity at each individual stage, making the overall system more efficient and manageable.
Solution Approach 2:
The system performs preliminary color space transformation and gamma correction to simplify the input data before applying segmentation algorithms. By pre-processing the video to create a perceptually uniform color representation with corrected gamma encoding, the subsequent segmentation requires less computational complexity while maintaining high automation levels, as the preprocessing steps handle the computationally intensive color analysis.
Data Source
AI summary
Examples described herein relate to automatic identification and transformation of a color region. A user can identify a region of a video frame or image that corresponds to a color region that is to be segmented. A color region can include one or more colors that appear to be approximately a uniform color. For one or more video frames, gamma correction can be applied to frames of the video. One or more frames of a video can be mapped to two color spaces. For each pixel in an image, a determination is made if the pixel has the same color as that of the identified region based on each of the at least two color spaces identifying the pixel as the color. The color region can be identified throughout a video and transformed to another color to aid in video editing.