Multi-Resolution Image Storage for Large Sparse Dataset Retrieval
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Solution Overview
Problem
High-resolution digital images with large file sizes are challenging to process due to memory and processor limitations, and displaying and analyzing them is computationally difficult, especially when zooming in and out to view different features.
Innovation Solution
A method for efficiently storing and retrieving high-resolution images by representing objects and their numerical features in a multi-dimensional space using tabular and semi-sparse matrix storage, allowing for incremental addition and reconstruction of image data at different resolutions, and integrating machine learning for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution digital images are stored in traditional formats, then image quality is preserved, but memory and processor limitations make processing difficult
Solution Approach 1:
The image is divided into multiple tiles or blocks that can be processed independently. Each tile represents a manageable portion of the overall high-resolution image, allowing processing systems to handle smaller data units without losing the ability to reconstruct the complete high-quality image when needed.
Solution Approach 2:
The patent introduces multiple resolution levels as an additional dimension to the traditional single-resolution storage approach. Images are stored at various resolution levels (e.g., full resolution, half resolution, quarter resolution), enabling the system to select appropriate processing detail based on the specific task requirements, thereby reducing overall processing complexity.
2Manufacturing precision
If full resolution images are stored for all regions, then detailed analysis is enabled, but storage space is inefficiently used
Solution Approach 1:
Different regions of the image are stored at different resolution levels based on their importance and the likelihood of requiring detailed analysis. Critical regions containing objects of interest are maintained at full resolution, while less important background or empty regions are stored at lower resolutions, optimizing the balance between analysis precision and storage efficiency.
Solution Approach 2:
Instead of storing complete full-resolution data for the entire image, the system stores only the necessary portions at full resolution. Machine learning models can retrieve and process only the relevant high-resolution regions when needed, rather than loading and processing the entire high-resolution image, thus reducing storage requirements while maintaining analysis capability where necessary.
3Productivity
If high-resolution images are loaded into memory for processing, then complete image analysis is possible, but random access memory limitations are exceeded
Solution Approach 1:
The image processing workflow is segmented into tile-based operations where only relevant tiles are loaded into memory at any given time. This allows the system to perform complete analysis on the portions of the image that are currently in memory, while other regions remain on storage media, effectively extending the usable memory capacity beyond physical RAM limits.
Solution Approach 2:
The system utilizes multiple resolution levels as an additional dimension to manage memory usage. When processing large images, lower-resolution versions can be loaded entirely into memory for operations that don't require full detail, while full-resolution data is selectively loaded only for specific regions requiring detailed analysis, thus staying within memory capacity constraints.
4Ease of manufacture
If traditional storage methods are used, then data is easily stored, but retrieval and reconstruction of image regions is slow
Solution Approach 1:
The image data is organized into discrete tiles with associated metadata including spatial coordinates and resolution level information. This segmentation enables the retrieval system to quickly locate and extract only the specific tiles needed for a given operation, rather than reading through entire images or large data structures, significantly improving retrieval speed while maintaining storage organization.
Solution Approach 2:
Image data is pre-processed and organized into multiple resolution levels and tile segments during the storage phase. This preliminary organization includes creating indexes and metadata structures that enable rapid location and retrieval of specific regions at appropriate resolutions, eliminating the need for time-consuming processing during retrieval operations.
5Reliability
If machine learning analysis is performed on entire high-resolution images, then comprehensive object detection is achieved, but computational overhead is excessive
Solution Approach 1:
The machine learning analysis process is divided into tile-based operations where individual models or model instances process small image tiles independently in parallel. This segmentation reduces the computational burden on any single processing unit, enables distributed computing across multiple processors or devices, and maintains detection accuracy by ensuring each tile receives adequate processing attention.
Solution Approach 2:
Machine learning models are applied selectively to only those image regions that contain or are likely to contain objects of interest, rather than uniformly processing the entire high-resolution image. This partial application of computational resources significantly reduces overall computational overhead while maintaining comprehensive detection coverage through strategic selection of processing regions.
Data Source
AI summary
Methods, apparatus, and systems for high-resolution image storage and analysis are disclosed. In one disclosed embodiment, a method includes analyzing a high-resolution image; partitioning the high-resolution image into logical blocks; detecting objects in the high-resolution image within the logical blocks, assigning objects that differ or with differing measures to differing layers, and assigning objects that are similar or with similar measures to the same layer; tabulating the detected objects into a table, wherein each row indicates a spatial position of each detected object and at least one measure of each detected object; storing the spatial position and the at least one measure of each detected object into the table; spatially rendering selected objects over the high-resolution image; and displaying the selected objects at a selected resolution within a portion of the high-resolution image on a display device.