Multi-Resolution Image Processing for Dynamic Object Recognition
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Solution Overview
Problem
Conventional image compression techniques compress entire images into a constant resolution, leading to inefficiency, increased file size, and higher storage and transmission costs, especially when handling high-resolution images with dynamic objects.
Innovation Solution
A multi-resolution image processing device and method that identifies dynamic objects, assigns IDs, determines authenticity, extracts feature points, and compresses images with varying compression rates for regions of interest and other areas, allowing for efficient storage and transmission by prioritizing high resolution for important regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the entire image is compressed into a constant resolution, then the compression process is simple, but the file size increases and storage/transmission costs increase
Solution Approach 1:
The patent applies different compression rates to different regions of the image based on their importance. Regions containing dynamic objects are compressed at lower rates to preserve quality, while static background regions are compressed at higher rates. This local differentiation resolves the contradiction by reducing overall file size without sacrificing critical image quality.
Solution Approach 2:
The image is segmented into multiple regions based on motion detection and object identification. The compression process then treats each segment differently, applying appropriate compression rates based on the segment's content. This segmentation approach enables selective compression that reduces file size while maintaining quality where needed.
2Device complexity
If high-resolution images are compressed with fixed resolution, then the compression algorithm is simple, but transmission traffic increases and storage capacity requirements increase
Solution Approach 1:
Different compression rates are applied to different image regions based on their importance. Dynamic object regions use lower compression rates to preserve detail, while static regions use higher compression rates to reduce data. This approach reduces transmission traffic without requiring complex full-image processing.
Solution Approach 2:
The compression rate is made dynamic rather than fixed, adjusting based on the content of each image region. Motion detection and object identification results dynamically determine the compression rate for each region, enabling efficient transmission by adapting compression to actual image content.
3Ease of operation
If conventional compression is applied to high-resolution images with dynamic objects, then the processing is straightforward, but important regions lose image quality
Solution Approach 1:
The patent identifies important regions containing dynamic objects and applies lower compression rates specifically to these areas. This preserves image quality in critical regions while allowing higher compression in less important static regions, resolving the contradiction between processing simplicity and quality preservation.
Solution Approach 2:
Motion detection and object identification are performed before compression to pre-identify regions that require quality preservation. This preliminary action enables the compression process to automatically protect important regions without complex real-time adjustments during compression.
4Quantity of substance
If the entire image is compressed at high compression rate, then storage space is reduced, but the time required for search and retrieval increases
Solution Approach 1:
By preserving higher quality in regions containing dynamic objects through lower compression rates, the patent enables more efficient search and retrieval. Important visual features in dynamic object regions remain intact, facilitating faster identification and search compared to uniformly highly-compressed images.
Data Source
AI summary
According to an embodiment of the present invention, a multi-resolution image processing device capable of recognition of a plurality of dynamic objects comprises a dynamic object identifier extracting a motion vector from an input image and identifying a dynamic object; a dynamic object ID imparter assigning an ID to the dynamic object identified by the dynamic object identifier; a dynamic object artificial intelligence determiner determining an authenticity of the dynamic object identified by the dynamic object identifier based on a standard shape for each type of dynamic object learned and stored, classifying at least one object of interest and object of no interest designated by a user, and removing the object of no interest; a region-of-interest detector extracting a feature point from the at least one object of interest and setting at least a portion of the object of interest as a region of interest; and a variable compressor compressing the image with different compression rates for the region of interest and the object of interest.