Real-Time Video Color Correction via Segmentation and Histogram Equalization
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Solution Overview
Problem
Existing video communication technologies face challenges in modifying images within a video stream in real-time, particularly under poor lighting conditions or with unsatisfactory color ratios, and lack automated systems for color adjustment or correction during video capture.
Innovation Solution
An automated image segmentation and color extraction system that identifies and modifies color values in real-time within a video stream, specifically targeting areas of interest like facial features, by converting images to cylindrical coordinate representations, performing histogram equalization, and applying color corrections to ensure uniform color ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated image segmentation and color extraction is implemented in real-time video streams, then color correction and modification capability is improved, but device complexity and processing requirements increase
Solution Approach 1:
The video stream is divided into discrete frames, and each frame is segmented into regions of interest (such as skin tones, clothing, background) using image segmentation algorithms. This allows selective color correction on specific regions rather than processing the entire video stream uniformly, reducing overall processing complexity while maintaining automated color correction capability.
Solution Approach 2:
The system transforms video data from standard RGB color space to cylindrical coordinate representations (HSV or LAB color spaces), enabling independent manipulation of hue, saturation, and brightness parameters. This parameter separation allows automated color correction to adjust specific color properties without affecting others, simplifying the correction process while improving automation extent.
2Manufacturing precision
If color values are modified in real-time during video capture, then image quality and visual experience are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs histogram equalization and color profile analysis on video frames in advance, before final color correction is applied. By pre-processing frames to normalize color distributions and identify dominant colors, the system reduces the computational burden of subsequent real-time color correction, maintaining color accuracy while reducing processing time.
Solution Approach 2:
The system applies color correction selectively to regions of interest (such as skin tones or primary subjects) rather than uniformly processing the entire video frame. This partial action approach focuses computational resources on areas where color accuracy has the greatest impact on visual experience, reducing overall processing time while maintaining high color precision where needed.
3Stability of the object's composition
If histogram equalization and cylindrical coordinate conversion are applied to all video frames, then color uniformity is improved, but processing speed decreases
Solution Approach 1:
The system performs full histogram equalization and cylindrical coordinate conversion periodically on selected key frames rather than on every video frame. Between these periodic processing intervals, lighter color adjustment operations are applied to maintain consistency. This periodic approach ensures color uniformity is achieved at regular intervals while maintaining high video processing speed during intermediate frames.
Solution Approach 2:
The system applies different processing intensities to different regions of video frames based on their importance and color characteristics. Regions with significant color variations or skin tones receive full histogram equalization and cylindrical coordinate processing, while uniform or less important regions receive simplified color adjustment. This local quality approach maintains color consistency where needed while preserving overall processing speed.
Data Source
AI summary
Systems, devices, media, and methods are presented for segmenting an image of a video stream with a client device, extracting one or more color from the image and modifying the video stream. The systems, devices, and method convert images of the set of images to a coordinate representation, perform a histogram equalization, identifies one or more colors of the coordinate representation based on an area of interest, determines a prevailing color, and applies the prevailing color to pixels of the video stream.