Multi-Exposure Video Blending for HDR Ghosting Reduction
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Solution Overview
Problem
Existing video camera systems struggle with ghosting and limited dynamic range in multi-track recording, particularly in high dynamic range (HDR) and virtual digital video production environments, which affect the quality of captured images.
Innovation Solution
A video camera system with an image sensor and processors that capture digital video frames at different exposure levels, analyze blending parameters to minimize ghosting, and generate blended pixels with higher dynamic range, enabling multi-track recording and real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If multi-track recording is implemented to capture video frames at different exposure levels, then dynamic range is improved, but ghosting artifacts are introduced
Solution Approach 1:
The patent applies local quality by analyzing each pixel individually to determine blending parameters. For each pixel, the system calculates a blending parameter based on the difference between exposure levels and the intensity of the pixel, then uses this parameter to blend the multi-track recordings. This localized approach allows different regions of the image to be blended differently, reducing ghosting in high-contrast areas while maintaining dynamic range benefits.
Solution Approach 2:
The patent changes the blending parameter dynamically for each pixel based on the exposure level differences and pixel intensity. The blending parameter is calculated as a function of the exposure level difference and the pixel intensity, allowing the system to adjust the blending ratio in real-time. This parameter change enables the system to optimize the balance between dynamic range and ghosting reduction for each specific pixel.
2Object-affected harmful factors
If blending parameters are adjusted to reduce ghosting, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating the blending parameter based on the exposure level difference and pixel intensity before actually performing the blending operation. The blending parameter is determined in advance using the formula that considers exposure level difference and pixel intensity, which simplifies the subsequent blending process and reduces real-time processing complexity.
Solution Approach 2:
The patent replaces complex real-time analysis with a more straightforward mathematical calculation. Instead of performing complex image analysis or machine learning-based ghosting detection during video processing, the system uses a direct mathematical formula that calculates the blending parameter based on exposure level differences and pixel intensity values, significantly reducing processing complexity while maintaining effectiveness.
3Illumination intensity
If exposure levels are varied to capture high dynamic range content, then detail in shadows and highlights is improved, but blending between tracks becomes more difficult
Solution Approach 1:
The patent changes the blending parameter dynamically based on the specific characteristics of each pixel and the exposure level differences. The blending parameter is calculated as a function of the exposure level difference and pixel intensity, which allows the system to automatically adjust the blending ratio to optimize the combination of tracks. This adaptive parameter change makes the blending process more straightforward by using direct mathematical relationships rather than complex algorithms.
Solution Approach 2:
The patent incorporates feedback by using the pixel intensity and exposure level difference information to determine the blending parameter. The system continuously monitors the characteristics of each pixel and adjusts the blending parameter accordingly, creating a feedback loop that optimizes the blending process. This feedback mechanism simplifies the overall process by using the inherent information already present in the image data.
Data Source
AI summary
A video camera system has a multi-track recording mode in which captured digital video frames each have a first sub-frame having a first exposure level and a second sub-frame having a second exposure level different than the first exposure level. For each respective frame of a plurality of digital video frames in the stream, for each respective pixel of a plurality of pixels in the frame, the camera is configured to analyze pixel data for the respective pixel from the first sub-frame and pixel data for the respective pixel from the second sub-frame to determine whether to adjust an amount of blend from a first blending amount to a second blending amount. The camera uses the amount of blend to blend the respective pixel of the first sub-frame together with the respective pixel of the second sub-frame to generate a blended pixel.