Clean Reference Image Generation for Chroma Keying
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Solution Overview
Problem
Chroma keying techniques often fail to accurately replace backgrounds due to the absence of a clean reference image, especially when capturing such an image is impractical, such as with heavy foreground objects or after filming is completed.
Innovation Solution
A method to automatically generate a clean reference image by performing a color census, selecting a color profile, identifying foreground areas, and applying colors to noise or foreground details based on non-identified image areas, allowing for accurate background replacement during chroma keying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a clean reference image is captured during filming, then chroma keying accuracy is improved, but the process becomes impractical when heavy foreground objects are present or filming is completed
Solution Approach 1:
The system performs preliminary analysis of the composite image to identify and remove foreground objects computationally, creating a clean reference image without requiring a separate preliminary shot. This eliminates the need for practical constraints during filming while maintaining chroma keying accuracy.
Solution Approach 2:
The system creates a computational copy of the background by analyzing color distributions and patterns in the composite image, then uses this synthesized reference image for chroma keying operations, replacing the need for a physical reference shot.
2Productivity
If automated processing is implemented to generate clean reference images, then user intervention is eliminated and processing efficiency is improved, but algorithm complexity increases
Solution Approach 1:
The system performs self-service by automatically analyzing the composite image, identifying foreground objects through color profile comparison, and generating the clean reference image without any user intervention. The algorithm serves itself by using the input image to create the processing tool it needs.
Solution Approach 2:
The system changes parameters by comparing pixel colors against established color profiles and using threshold-based filtering to automatically distinguish foreground from background, enabling automated processing through parameter-driven decision making.
3Manufacturing precision
If foreground areas are accurately identified and removed, then clean reference image quality is improved, but processing time increases
Solution Approach 1:
The system applies partial action by focusing computational effort only on areas where foreground objects are detected, rather than processing the entire image uniformly. This selective approach maintains image quality while reducing overall processing time.
Data Source
AI summary
The present disclosure includes, among other things, systems, methods and program products for generating a clean reference frame image. A method includes obtaining a color census of a color image. A color profile is selected based on the color census. One or more foreground areas of the image are identified, the foreground areas comprising image areas containing colors that do not fit the color profile. For each identified foreground area, a color is applied to the identified foreground area based on one or more colors of one or more non-identified image areas to create a clean reference image.