Mobile Image Capture Device Selective Retention Neural Network
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Solution Overview
Problem
Mobile image capture devices face resource constraints such as limited memory, processing power, and energy, which hinder their ability to continuously capture and store high-resolution images without overheating, making it challenging to efficiently manage image storage and retention.
Innovation Solution
A resource-efficient mobile image capture device equipped with a neural network-based scene analyzer that selectively retains images by assessing their desirability, adjusting capture modes, and optimizing energy consumption, allowing for continuous image capture and intelligent storage decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the mobile image capture device continuously captures and stores high-resolution images, then the image quality and quantity are improved, but the memory resources are depleted and the device overheats
Solution Approach 1:
The patent applies preliminary action by analyzing image frames and predicting future frame quality before storing images. The system uses a machine learning model to assess the desirability of upcoming frames, allowing the device to prepare for selective storage in advance, thereby optimizing memory usage while maintaining high capture rates.
Solution Approach 2:
The patent extracts only the most valuable image frames for storage based on quality assessment. By using a machine learning model to identify and extract only the desirable frames from the continuous stream of captured images, the system significantly reduces memory consumption while preserving the most important visual moments.
2Quantity of substance
If the mobile image capture device continuously processes and compresses images, then the image storage capability is improved, but the power consumption increases
Solution Approach 1:
The patent applies partial action by processing and compressing only the selected high-quality images rather than all captured frames. The machine learning model identifies a subset of valuable frames, and only these frames undergo compression and storage processing, significantly reducing the total computational workload and power consumption while maintaining adequate storage capacity.
3Manufacturing precision
If the mobile image capture device operates at full processing power, then the image capture quality is improved, but the thermal power dissipation exceeds limits causing overheating
Solution Approach 1:
The patent uses preliminary action by pre-assessing frame quality using a lightweight machine learning model before committing to full processing. This allows the system to identify high-value frames in advance and allocate full processing power only to those frames, rather than continuously operating at maximum power, thereby reducing thermal dissipation while maintaining image quality.
4Reliability
If the mobile image capture device stores all captured images, then the image retention rate is improved, but the memory resources are depleted quickly
Solution Approach 1:
The patent extracts only the most valuable frames for permanent storage based on quality assessment. By using a machine learning model to identify and extract desirable frames from the continuous image stream, the system achieves high retention rates for important moments while consuming minimal memory resources.
Solution Approach 2:
The patent changes the selection parameter from storing all frames to storing only frames that meet a quality threshold determined by a machine learning model. This parameter change allows the system to maintain high retention rates for valuable images while dramatically reducing overall memory consumption.
Data Source
AI summary
The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. The mobile image capture device is operable to input an image into at least one neural network and to receive at least one descriptor of the desirability of a scene depicted by the image as an output of the at least one neural network. The mobile image capture device is operable to determine, based at least in part on the at least one descriptor of the desirability of the scene of the image, whether to store a second copy of such image and/or one or more contemporaneously captured images in a non-volatile memory of the mobile image capture device or to discard a first copy of such image from a temporary image buffer without storing the second copy of such image in the non-volatile memory.