Image Storage via Pixel Reordering and Sub-Image Distribution
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Solution Overview
Problem
Data storage providers face increasing costs for storage capacity and processing power due to rising data usage from smart devices, necessitating a reduction in storage capacity while maintaining data storage reliability.
Innovation Solution
Implementing an image reconstruction approach that uses sub-images, reorders pixels to make them non-adjacent, groups them into sub-images, creates parity blocks, and stores each sub-image and parity block on separate nodes, allowing for greater node failures while maintaining reliability and reducing reconstruction costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If storage capacity is increased to accommodate rising data usage, then data storage reliability is improved, but storage cost increases
Solution Approach 1:
The patent divides an image into multiple sub-images and stores them across different nodes. This segmentation allows the system to tolerate node failures while maintaining data availability, thereby improving reliability without requiring complete redundancy of the entire image, thus reducing storage costs.
Solution Approach 2:
The patent changes the resolution parameter of images during storage and retrieval. By storing images at lower resolution and allowing reconstruction at higher resolution when needed, the system reduces the amount of storage capacity required while maintaining the ability to provide high-quality images on demand, thus improving reliability without proportionally increasing storage costs.
2Reliability
If storage capacity is increased to accommodate rising data usage, then data storage reliability is improved, but processing power requirements increase
Solution Approach 1:
By segmenting images into sub-images distributed across multiple nodes, the patent enables parallel processing during retrieval operations. This reduces the processing burden on any single server while maintaining data availability, thus improving reliability without requiring proportionally increased processing power.
Solution Approach 2:
The patent retrieves images at lower resolution from distributed nodes and reconstructs them to higher resolution only when necessary. This approach significantly reduces the processing power required for routine retrieval operations while maintaining the capability to provide high-quality images, thus improving reliability without increasing processing power requirements.
3Manufacturing precision
If full-resolution image reconstruction is performed, then image quality is maintained, but reconstruction cost increases
Solution Approach 1:
The patent implements dynamic resolution parameter changes by storing and initially retrieving images at lower resolution. Full-resolution reconstruction is performed only when explicitly required, reducing the overall reconstruction cost while maintaining image quality when needed.
Solution Approach 2:
The patent applies partial reconstruction by retrieving only the necessary sub-images at the required resolution. Instead of always reconstructing full-resolution images from all sub-images, the system performs partial reconstruction using only the necessary components, thus reducing reconstruction cost while maintaining image quality for the required portion.
Data Source
AI summary
An apparatus comprises: a receiver configured to receive an image; and a processor coupled to the receiver and configured to: obtain pixels of the image; reorder the pixels to create reordered pixels; generate a first sub-image and a second sub-image using the reordered pixels; generate a first instruction to store the first sub-image in a first sub-image node; and generate a second instruction to store the second sub-image in a second sub-image node; and a transmitter coupled to the processor and configured to transmit the first instruction and the second instruction to a database.