Wearable HDR Photography With Split Compute for Battery Efficiency
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Solution Overview
Problem
Generating high dynamic range (HDR) images on wearable devices is challenging due to computational limitations and power constraints, leading to inefficient processing times and resource exhaustion.
Innovation Solution
A split compute approach where wearable devices perform initial image processing at a reduced resolution, aligning and merging images, while a companion device handles tone mapping and merging using machine learned models to generate HDR images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If HDR processing is performed entirely on the wearable device, then processing speed and quality may improve, but power consumption increases and battery life decreases
Solution Approach 1:
The HDR processing pipeline is segmented into two parts: initial image processing (alignment, merging, denoising) is performed on the wearable device at reduced resolution, while computationally intensive operations (tone mapping, final merging) are offloaded to the companion device. This segmentation reduces power consumption on the wearable device while maintaining processing quality.
2Manufacturing precision
If full-resolution images are processed for HDR generation, then image quality improves, but computational resources and processing time are exhausted
Solution Approach 1:
Different processing resolutions are applied to different stages of the pipeline: reduced resolution for initial processing on the wearable device, and full resolution for final processing on the companion device. This local quality approach maintains overall image quality while improving processing efficiency at each stage.
Solution Approach 2:
Preliminary processing steps (alignment, merging, denoising) are performed on reduced-resolution images before the final HDR generation. This preliminary action reduces the computational burden of subsequent full-resolution processing while maintaining the quality of the final HDR image.
3Measurement precision
If complex HDR algorithms are executed on the wearable device, then processing accuracy improves, but device complexity and resource requirements increase
Solution Approach 1:
Complex HDR algorithms are segmented between two devices: the wearable device handles simpler operations (alignment, merging, denoising), while the companion device handles more complex operations (tone mapping, final merging). This segmentation maintains processing accuracy while distributing computational complexity.
Data Source
AI summary
A method including capturing, by a wearable device, a plurality of images each having a first resolution, process, by the wearable device, the plurality of images to generate a first image having a second resolution, the second resolution being smaller than the first resolution, selecting, by the wearable device, a second image from the plurality of images having the first resolution based on a setting of the wearable device, and communicating, by the wearable device to a companion device, the first image and the second image. Further, processing, by the companion device, the first image, and merging, by the companion device, the processed first image with the second image to generate a high dynamic range (HDR) image.