Ambient Light-Tagged Raw Capture for Low-Power Hyperlapse
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Solution Overview
Problem
Consumer-grade image capture devices, including wearable devices and smartphones, are constrained by power and computing resources, making it difficult to sustain advanced capture modes like hyperlapse for extended periods without draining battery power.
Innovation Solution
Implementing a method where images are captured in raw format with concurrent ambient light sensing, stored, and processed in bulk to generate hyperlapse videos, offloading processing to devices with more resources, thereby reducing power consumption and information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If intensive image processing is performed on captured images to generate hyperlapse videos, then image quality and processing capability are improved, but power consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by capturing images in raw format with full information preservation before processing. The ambient light properties are detected and stored concurrently with image capture, preparing all necessary data in advance for later bulk processing. This allows the device to perform minimal processing during capture (low power) while maintaining the capability for intensive processing later when power is available.
Solution Approach 2:
The patent segments the processing workflow into two distinct phases: a capture phase where only minimal operations are performed (capturing raw images and ambient light data), and a post-processing phase where intensive processing occurs. This segmentation allows the device to separate low-power operations from high-power operations, enabling hyperlapse generation without continuous high power consumption during capture.
2Speed
If real-time image processing is performed during capture, then processing speed is improved, but power consumption and information loss increase
Solution Approach 1:
The system performs preliminary capture of raw image data and ambient light properties without immediate processing. By storing the ambient light properties concurrently with image capture, the system prepares all necessary information in advance, enabling efficient bulk processing later without losing temporal correlation data that would be needed for real-time processing.
Solution Approach 2:
The patent creates a copy of the ambient light properties data structure that mirrors the image capture timeline. This copied data structure allows post-processing to reconstruct the temporal and environmental context without requiring the original sensor to remain active or perform real-time processing, thereby reducing power consumption while maintaining processing capability.
3Measurement precision
If ambient light properties are detected and stored for each captured image, then post-processing accuracy is improved, but data storage requirements increase
Solution Approach 1:
The patent applies local quality by storing ambient light properties specifically associated with each image capture event rather than continuous light data. The ambient light sensor properties are stored at discrete points corresponding to image captures, providing precise local environmental context where needed while avoiding redundant storage of light data between captures, thus optimizing storage efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables ultra-low power image capture and more advanced post-processing, preserving battery life while maintaining image quality and consistency in hyperlapse videos.
Implementation Method 1
An ambient light sensor (ALS) is configured to detect associated ambient light properties for at least some of the captured images
Data Source
AI summary
Hyperlapse imaging is described. A device may include a camera, a sensor and a data store. The camera is configured to capture images. The sensor detects ambient light properties. Captured images and the ambient light properties are processed in bulk to generate a hyperlapse video.