Multi-Processor Array Imager Encoding for Storage Reduction
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Solution Overview
Problem
The increasing amount of information in digital images poses a substantial burden on computing resources, leading to slow device operation, reduced user friendliness, and increased user frustration when storing and transmitting sets of images.
Innovation Solution
Implementing multi-processor support for array imagers, where multiple processors encode images using various image-compression techniques such as spatial, temporal, and spectral prediction, allowing for efficient storage and transmission of image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple images are captured and stored in uncompressed format, then image quality and information content are preserved, but storage size and computational burden increase substantially
Solution Approach 1:
The patent divides the image processing task into multiple segments by using multiple processors, where each processor handles specific images or image sets. This segmentation allows parallel compression of multiple images simultaneously, reducing the computational burden while maintaining compression effectiveness. The image data is divided into manageable portions that can be processed independently across multiple CPU cores.
Solution Approach 2:
The patent applies different compression parameters and techniques to different images based on their characteristics. By analyzing image properties and adjusting compression levels accordingly, the system optimizes the balance between storage size reduction and information preservation. This adaptive parameter adjustment allows effective compression without excessive loss of important image data.
2Device complexity
If image compression is performed using a single processor, then processing simplicity is maintained, but processing speed and productivity are reduced
Solution Approach 1:
The patent segments the image compression workload across multiple processors, with each processor responsible for compressing specific images or image sets. This distribution of tasks significantly increases processing throughput while maintaining manageable complexity through modular processor assignments. The system can dynamically assign images to available processors based on workload requirements.
Solution Approach 2:
The patent introduces a multi-dimensional processing architecture where multiple processors operate simultaneously on different image datasets. This dimensional expansion from single-processor sequential processing to multi-processor parallel processing dramatically increases productivity without proportionally increasing overall system complexity.
3Loss of information
If image data is transmitted in large sizes, then complete image information is conveyed, but transmission time and network burden increase
Solution Approach 1:
The patent optimizes transmission parameters by compressing image data to appropriate bit depths and resolution levels before transmission. This parameter adjustment reduces the amount of data that needs to be transmitted while preserving essential image information for the intended application, thereby decreasing transmission time without significant information loss.
Solution Approach 2:
The patent performs compression and optimization of image data before transmission occurs. By preparing and compressing images in advance, the system reduces the volume of data that needs to be transmitted in real-time, thereby decreasing transmission time and network burden while maintaining information完整性.
4Quantity of substance
If computational resources are allocated to compression, then storage size is reduced, but processing time and energy consumption increase
Solution Approach 1:
The patent segments the compression workload across multiple processors, allowing parallel processing of multiple images simultaneously. This distribution reduces the computational energy required per processor while achieving the same overall compression effect. The segmented approach prevents any single processor from being overloaded, optimizing energy efficiency across the system.
Solution Approach 2:
The patent adjusts compression parameters to achieve optimal balance between storage size reduction and computational energy consumption. By selecting appropriate compression levels and techniques based on image characteristics and storage requirements, the system minimizes unnecessary computational energy while achieving effective size reduction.
Data Source
AI summary
Using the techniques discussed herein, a set of images is captured by one or more array imagers (106). Each array imager includes multiple imagers configured in various manners. Each array imager captures multiple images of substantially a same scene at substantially a same time. The images captured by each array image are encoded by multiple processors (112, 114). Each processor can encode sets of images captured by a different array imager, or each processor can encode different sets of images captured by the same array imager. The encoding of the images is performed using various image-compression techniques so that the information that results from the encoding is smaller, in terms of storage size, than the uncompressed images.