Map Area Detection in Image Compression Using Binary and Thin-Line Ratios
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Solution Overview
Problem
Conventional methods for compressing image data struggle to accurately distinguish map areas from other areas, leading to illegible characters in maps during high-compression processes, as they often misclassify areas with similar darkness distribution properties or require extensive processing.
Innovation Solution
A method involving the generation of binary and thin-line images to calculate pixel ratios, determining whether an area is a map or photograph based on these ratios, and applying appropriate compression techniques, including no resolution reduction for maps to maintain legibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high compression rate is applied to map areas, then file size is reduced, but character legibility deteriorates
Solution Approach 1:
The patent applies different compression rates to different areas of the image based on their content type. Map areas are identified and compressed at a lower rate to preserve character legibility, while non-map areas are compressed at higher rates. This local differentiation resolves the contradiction by optimizing compression for each region's specific requirements.
Solution Approach 2:
The patent performs preliminary identification of map areas before compression is applied. By detecting map regions in advance using image analysis techniques, the system can pre-determine which areas require legibility preservation, allowing appropriate compression strategies to be applied subsequently without compromising character readability in maps.
2Adaptability or versatility
If conventional map detection methods are used, then map areas can be identified, but detection accuracy deteriorates due to misclassification of areas with similar darkness distribution
Solution Approach 1:
The patent changes the detection parameters from simple darkness distribution analysis to a more sophisticated multi-parameter approach. By analyzing additional image characteristics beyond darkness levels, the system can distinguish map areas from other regions with similar darkness properties, thereby improving detection accuracy and reducing misclassification.
3Quantity of substance
If high compression rate is applied to entire image, then file size is reduced, but overall image quality deteriorates
Solution Approach 1:
The patent implements local quality preservation by identifying specific regions (map areas) that require higher quality retention and applying differentiated compression strategies. Non-map areas receive aggressive compression while map areas are preserved with higher quality, achieving overall file size reduction without compromising important visual information.
Data Source
AI summary
First, a binary image is generated by binarizing an image. Next, a binary pixel ratio, that is a ratio of a binary pixel quantity that is a quantity of dotted pixels included in a specific area of the binary image to a total quantity of pixels included in the specific area of the binary image, is found. Then, a thin-line image is generated by performing a line-thinning process on the specific area. After that, a thin-line pixel ratio that is a ratio of the quantity of dotted pixels included in the generated thin-line image to the binary pixel quantity is found, and the specific area is determined to be a map area or a photograph area based on the calculated binary pixel ratio and the calculated thin-line pixel ratio.