Image storage method, image storage device and computer storage medium

By generating visual saliency maps for region segmentation and adaptive encoding, the problem of balancing storage efficiency and visual quality in existing technologies is solved, achieving efficient image storage and retrieval.

CN121705445AActive Publication Date: 2026-03-20数盾信息科技股份有限公司
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Patent Information

Application Number
CN202610194621.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-11
Publication Date
2026-03-20
Estimated Expiration
2046-02-11

AI Technical Summary

Technical Problem

Existing image storage methods ignore the spatial importance differences of image content, resulting in loss of detail in key areas or redundant storage in secondary areas. This makes it difficult to achieve the optimal balance between storage efficiency and visual quality, and they also lack structured metadata and efficient retrieval capabilities.

Method used

By calculating local contrast and edge density to generate a visual saliency map for region segmentation, and combining it with an adaptive rate-distortion benchmark value for encoding, a region mapping index and an inverted index are constructed to achieve content adaptive encoding and efficient retrieval.

Benefits of technology

It achieves region segmentation that conforms to human visual attention characteristics, dynamically adjusts the encoding strategy, improves the balance between storage efficiency and visual quality, and accelerates the image retrieval and analysis process.

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Abstract

The invention discloses an image storage method, image storage equipment and a computer storage medium, and relates to the technical field of image processing and storage. The method comprises the following steps: acquiring original data through an image acquisition sensor, calculating local contrast and edge density, performing fusion to generate a visual saliency map, and performing region segmentation to obtain an average visual saliency value of each region; calculating a statistical variance and a global peak value according to the values of all the areas, and generating an adaptive rate distortion reference value; comparing each region value with a reference value to determine a quantization step size and a visual saliency level, and performing adaptive coding on the region to form a composite data stream; calculating the proportion of the geometric center to the area of each region, packaging the proportion into metadata, and packaging and storing the metadata and the coding stream; and constructing a mapping index from the region identifier to the physical address and a reverse index based on the visual saliency level. The method can improve the utilization rate of the storage space and optimize the image quality of the important region.
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