Scalable Image Storage Format Hierarchical Tile Segmentation
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Solution Overview
Problem
Current methods for storing and retrieving large datasets, such as 3D image data, require significant resources and time, and are inefficient due to the need for large hardware investments and sequential data organization, which limits access speed and scalability.
Innovation Solution
A scalable image storage format (SISF) that divides image data into sets of arrays with different resolution sizes, allowing for efficient storage and retrieval by generating an image data array, sets of first division arrays, and sets of second division arrays with smaller resolution sizes, stored in a memory according to the order of the first division arrays, along with an index map for quick data location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional storage methods are used for large image datasets, then data can be stored, but access speed is slow and significant resources and time are required
Solution Approach 1:
The patent divides large image datasets into multiple smaller tiles or chunks, organizing them in a hierarchical structure. This segmentation allows the system to access only relevant portions of data rather than scanning entire datasets, dramatically improving access speed and reducing time loss for storage and retrieval operations.
2Quantity of substance
If large hardware investments are made for storage, then data capacity increases, but system complexity and resource requirements increase
Solution Approach 1:
The patent introduces a hierarchical organization structure with multiple levels (datasets, collections, studies, series, instances, tiles). This multi-dimensional organization allows efficient management of large data capacities without proportionally increasing hardware complexity, as the structure enables logical organization and access patterns that reduce the need for complex hardware configurations.
3Productivity
If sequential data organization is used, then storage is simple, but access speed is limited and scalability is restricted
Solution Approach 1:
By segmenting data into hierarchical tiles and chunks, the system enables parallel processing and random access to specific data regions. This segmentation dramatically improves productivity for data processing tasks while maintaining ease of operation through the structured organization that simplifies data location and retrieval operations.
Solution Approach 2:
The hierarchical structure allows dynamic access patterns where the system can adaptively navigate through different levels of organization based on access requirements. This dynamic capability enables efficient scalability from small to large datasets without changing the fundamental access interface, maintaining ease of operation while improving productivity.
Data Source
AI summary
Methods and systems for storing and accessing image data in a scalable format are disclosed herein. An example method includes (i) generating an image data array representative of one or more images captured by one or more cameras associated with an apparatus; (ii) generating a set of first division arrays, each first division array representative of a different subset of the image data array and sharing a first resolution size; (iii) generating a set of second division arrays, each second division array representative of a different subset of one of the first division arrays in the set of first division arrays and sharing a second resolution size smaller than the first resolution size; and (iv) storing, in a memory, the set of second division arrays such that the set of second division arrays is stored according to an order of the set of first division arrays.