Adaptive Image Encoding for XML Compatibility
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Solution Overview
Problem
Digital image files in formats like JPEG, GIF, and PNG are not compatible with XML, requiring cumbersome re-encoding processes that increase processing time and network bandwidth during electronic transmission.
Innovation Solution
A method to transform bitmap image files into vector image files compatible with XML by recursively subdividing rectangular regions based on spectral characteristics and determining split points to minimize information loss, ultimately converting the image into Scalable Vector Graphics (SVG) format for efficient transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital image files are converted to XML-compatible format using traditional re-encoding processes, then XML compatibility is achieved, but processing time increases and network bandwidth demands increase
Solution Approach 1:
The image is divided into multiple rectangular sub-images through quadtree segmentation. Each sub-image is independently processed and encoded, allowing parallel computation and reducing overall processing time while maintaining XML compatibility through the structured vector representation of segments.
Solution Approach 2:
The patent changes the representation parameters from traditional bitmap pixel arrays to vector-based rectangular segment definitions with color information. This parameter transformation enables XML compatibility without requiring cumbersome base64 encoding, thereby reducing processing time and bandwidth requirements.
2Adaptability or versatility
If digital image files are converted to XML-compatible format using traditional re-encoding processes, then XML compatibility is achieved, but network bandwidth during transmission increases
Solution Approach 1:
The patent transforms image data from binary bitmap format to vector-based rectangular segment parameters including color information. This parameter change enables compact XML representation that maintains image fidelity while significantly reducing the amount of data transmitted, thereby lowering network bandwidth requirements compared to base64 encoding methods.
Solution Approach 2:
The patent extracts essential image information into discrete rectangular segments with associated color data, eliminating redundant pixel information. This extraction process creates a condensed vector representation that can be efficiently encoded in XML, reducing the quantity of data needed for transmission while maintaining XML compatibility.
3Adaptability or versatility
If images are encoded in vector representation with rectangular segments, then XML compatibility is improved, but image fidelity may be compromised
Solution Approach 1:
The patent segments the image into multiple rectangular sub-images using quadtree division, where each segment is independently encoded with its color information. This segmentation approach maintains image fidelity by preserving detailed regional variations while achieving XML compatibility through the structured vector representation of segments.
Solution Approach 2:
The patent applies different encoding strategies to different regions of the image based on their spectral characteristics. By analyzing and adapting the encoding parameters locally for each rectangular segment, the method maintains high image fidelity in areas requiring detail while optimizing for XML compatibility in regions where simpler representations suffice.
Data Source
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AI summary
A system and method are described for encoding digital images for electronic transmission in a text-based Internet format. The method may comprise the steps of defining (34) a current rectangular region (120) of the image bitmap; quantifying (36) spectral characteristics of the current rectangular region, wherein the spectral characteristics quantify the frequency spectrum or amount of details or variations that are present in the current rectangular region; comparing (38) said spectral characteristics to a predetermined threshold value; selecting (42) a split point (122) of the current rectangular region by determining (86,88) a center-of-gravity of the rectangular region based on color values of pixels in the rectangular region (82) and an average luminance value of the rectangular region (84); dividing (44) the current rectangular region into four rectangular sub-regions (124,125,126,127) having different sizes and aspect ratios using the split point, in response to the spectral characteristics being greater than the predetermined threshold value (38); tagging (52) a current rectangular sub-region as a terminal rectangular region in response to the spectral characteristics being less than or equal to the predetermined threshold value (38); for each of the four rectangular sub-regions that was not tagged as a terminal rectangular region, comparing (46) the size of the rectangular sub-region to a predetermined threshold size; for each of the four rectangular sub-regions that was not tagged as a terminal rectangular region, tagging (48) the rectangular sub-region for further subdivision in response to the respective size of the rectangular sub-region being greater than the predetermined threshold size (46); for each of the four rectangular sub-regions that was not previously tagged as a terminal rectangular region, tagging (52) the rectangular sub-region as a terminal rectangular region in response to the respective size of the rectangular sub-region being less than or equal to the predetermined threshold size (46); defining (50) one of the non-terminal rectangular sub-regions (124,125,126,127) as the current rectangular region (124) and repeating the above steps until all rectangular regions (130,131,132,134) are tagged terminal; determining (56) dimensional and color characteristics for each of the terminal rectangular regions; mapping (60) the dimensional and color characteristics to a vector image; and converting the vector image to a text-based Internet format.