Machine Learning Color Consistency Across Videos
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Solution Overview
Problem
Existing solutions for automatically adjusting color consistency across videos fail to accurately account for local variations and unusual color mismatches, leading to suboptimal results in maintaining consistent color across videos captured from different cameras or under varying light conditions.
Innovation Solution
A machine-learning based color consistency application computes a feature vector including global and local features of both a reference and target video, using predictive models to determine and apply color parameters such as exposure, color temperature, and tint, ensuring consistent color characteristics across videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing automatic color adjustment solutions are used, then time consumption is reduced, but color consistency accuracy deteriorates due to ignoring local variations
Solution Approach 1:
The patent segments the video content into different tonal ranges (shadows, midtones, highlights) and applies separate color adjustments to each segment. This allows local variations in different regions of video frames to be detected and corrected individually, rather than applying a single global adjustment that would miss local differences.
Solution Approach 2:
The patent implements local quality by creating tonal segment-specific color parameters that are applied differently to different luminance regions of the video. Each tonal segment receives customized color adjustments based on its specific characteristics, ensuring that local variations in illumination and color cast are accurately corrected while maintaining overall color consistency.
2Measurement precision
If manual color adjustment is performed, then color consistency accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The patent implements self-service by automatically detecting tonal segments, analyzing their color characteristics, and generating appropriate color correction parameters without requiring manual editor intervention. The system processes videos autonomously through automated workflows that identify local variations and apply corrections, eliminating the need for time-consuming manual parameter adjustment while maintaining high color consistency accuracy.
Solution Approach 2:
The patent automatically determines and applies color parameters (brightness, exposure, color temperature, tint) for each tonal segment based on algorithmic analysis of the video content. This automated parameter determination replaces manual adjustment processes, achieving both high accuracy and efficiency by dynamically calculating optimal parameters rather than relying on manual trial-and-error methods.
3Device complexity
If global feature analysis is used, then processing complexity is reduced, but color consistency accuracy deteriorates due to ignoring local variations
Solution Approach 1:
The patent divides the video analysis process into segments based on tonal ranges (shadows, midtones, highlights). Each segment is analyzed separately to detect local color variations specific to that luminance range. This segmentation approach manages processing complexity by breaking down the complex task of analyzing entire video frames into more manageable tonal-specific analyses.
Solution Approach 2:
The patent adds a new dimension to color analysis by incorporating tonal range classification. Instead of analyzing only global color features, the system introduces tonal segment identification as an additional analytical dimension, allowing it to detect and correct local variations that would be invisible in global analysis alone.
4Device complexity
If algorithms that cannot account for unusual color mismatches are used, then processing simplicity is maintained, but color consistency accuracy deteriorates
Solution Approach 1:
The patent enhances the algorithm's capability to handle unusual color mismatches by dynamically adjusting multiple color parameters (brightness, exposure, color temperature, tint) for each tonal segment. This multi-parameter adjustment approach allows the system to accommodate a wide range of color variations and mismatches that simpler algorithms cannot handle, while maintaining manageable complexity through systematic parameter optimization.
Data Source
AI summary
Disclosed systems and methods use machine-learning techniques to determine a set of parameters that if applied to a target video, apply a color characteristic of a reference video to the target video. For example, a color consistency application executing on a computing device computes a feature vector including a representation of a reference video and a target video. The application determines a set of color parameters (e.g., exposure, color temperature, tint, etc.) by applying the feature vector to one or more predictive models trained to determine color consistency. The application generates a preview image by applying the parameters to the target video. The applying causes an adjustment of exposure, color temperature, or tint in the target video such that a color consistency of the adjusted target video is consistent with a color consistency of the reference video. The color consistency application provides settings to further adjust the parameters.