Composite Image Generation from Video Frames
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Solution Overview
Problem
Capturing the perfect moment in still images is challenging due to timing issues, and consumer-level video cameras produce lower quality images compared to still cameras, making it difficult to preserve desired moments, especially for display or sharing.
Innovation Solution
A framework for generating composite images from sequential video frames that analyzes and combines the best aspects of multiple frames based on importance weights, allowing for the creation of higher resolution images with enhanced background and preserved salient objects, which can be panoramic or focused on specific scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a still camera is used to capture high quality images, then image quality is improved, but the ability to capture the perfect moment is reduced due to timing constraints
Solution Approach 1:
The system performs preliminary action by capturing multiple video frames before the perfect moment occurs, then processes these frames afterward to extract the optimal image. This allows the system to have the perfect moment captured without missing it, while still achieving high image quality through selective frame extraction and composite image generation.
Solution Approach 2:
The system merges multiple video frames into a composite image that combines the best elements from each frame. By aligning and combining corresponding pixels from multiple frames, the system produces a high-quality image that captures the perfect moment while reducing noise and improving overall image quality.
2Reliability
If a video camera is used to ensure the moment is not missed, then the ability to capture the perfect moment is improved, but image quality deteriorates due to lower resolution and more noise
Solution Approach 1:
The system combines multiple lower-quality video frames into a single high-quality composite image. By averaging corresponding pixels across multiple frames and applying noise reduction algorithms, the system eliminates the noise and resolution limitations of individual video frames while preserving the temporal advantage of video capture.
Solution Approach 2:
The system creates multiple copies of the scene from different video frames and processes these copies to generate a superior final image. Each video frame serves as a copy that can be selectively used or combined based on its quality and temporal characteristics.
3Manufacturing precision
If multiple video frames are processed to create composite images, then image quality is improved, but processing complexity increases
Solution Approach 1:
The system segments the processing task into distinct stages: frame selection, alignment, pixel-level processing, and composite image generation. By dividing the complex processing into manageable segments, the system reduces overall complexity while maintaining high image quality output.
Solution Approach 2:
The system applies different processing strategies to different regions of the image based on local characteristics. Salient objects receive different treatment compared to background regions, allowing optimized processing that reduces overall complexity while maintaining high quality where it matters most.
Data Source
AI summary
A method described herein includes acts of receiving a sequence of images of a scene and receiving an indication of a reference image in the sequence of images. The method further includes an act of automatically assigning one or more weights independently to each pixel in each image in the sequence of images of the scene. Additionally, the method includes an act of automatically generating a composite image based at least in part upon the one or more weights assigned to each pixel in each image in the sequence of images of the scene.