Image Mosaic Generation Using Tile Scoring and KD-Tree Matching
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Solution Overview
Problem
Existing image mosaic generation techniques are limited by the need for a large number of tiles that match the color, gradient, and orientation of the underlying image, which can be time-consuming and resource-intensive, especially when processing entire images rather than thumbnails, and often result in suboptimal image matching due to processing the entire image data.
Innovation Solution
The method involves dividing an image into tiles, scoring each tile using discrete cosine or sine transforms on sampled data, selecting matching images from a dataset using a kd-tree data structure, and rendering the mosaic with thumbnails to reduce processing time and improve accuracy, allowing for quicker mosaic generation and zooming capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entire images are processed to generate mosaics, then image matching accuracy is improved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent divides the image into multiple tiles and processes each tile independently using discrete cosine or sine transforms. This segmentation allows parallel processing of image blocks, reducing overall processing time while maintaining matching accuracy through localized transform operations on each tile rather than the entire image.
Solution Approach 2:
The patent pre-computes and stores transform coefficients (such as DCT or DST coefficients) for image tiles before mosaic generation. These pre-computed values are saved and reused during mosaic creation, eliminating the need to perform computationally intensive transform operations repeatedly, thus significantly reducing processing time while preserving matching precision.
2Measurement precision
If discrete cosine or sine transforms are used for scoring tiles, then image matching accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent transforms image tiles using discrete cosine or sine transforms to convert spatial domain data into frequency domain coefficients. This parameter transformation allows for more accurate matching by comparing transform coefficients that capture essential image characteristics, while the transforms themselves are computationally efficient compared to other methods like discrete Fourier transforms.
Solution Approach 2:
The patent extracts only the most significant transform coefficients from the transformed tile data for scoring purposes. By selecting and using only the dominant coefficients rather than all transformed data, the method reduces computational complexity while maintaining matching accuracy, as the most significant coefficients contain the essential information for image similarity assessment.
3Manufacturing precision
If a large number of tiles are used to match color, gradient and orientation, then mosaic quality is improved, but resource consumption and processing time increase
Solution Approach 1:
The patent uses discrete cosine or sine transforms to compute tile scores, which provide a more efficient and accurate method for assessing tile suitability compared to processing all image data. This partial action approach focuses computational effort on the most relevant features through transform coefficients, achieving high mosaic quality with fewer tiles by concentrating on the most significant image characteristics.
Data Source
AI summary
Apparatus, methods and systems for creating mosaics are described. A method is provided that includes identifying an image for presentation as a mosaic; dividing the identified image into a plurality of tiles; scoring each tile; identifying a matching image from an image dataset using the score for each tile; and rendering the mosaic using the matching images.