Image Segmentation for High-Resolution Rendering
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Solution Overview
Problem
Existing image processing libraries, such as the Adobe PDF Library, are limited by the use of 32-bit fixed point integer values, which restrict the size and resolution of images that can be processed, making it difficult to render large or high-resolution images effectively.
Innovation Solution
The approach involves segmenting images into smaller tiles, shifting each tile so that it can be processed using 32-bit fixed point integer values, and then repositioning the rendered tile data to its original location, allowing the use of existing library functions that rely on fixed point integers to handle transformations and rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If 32-bit fixed point integer values are used for image processing, then existing library functions can be utilized, but the size and resolution of processable images are restricted
Solution Approach 1:
The patent divides a large image into multiple smaller tiles that can be processed individually using 32-bit fixed point integer values. Each tile is processed separately and then repositioned to reconstruct the complete large image, thereby overcoming the size limitations of fixed point arithmetic while maintaining compatibility with existing library functions.
2Ease of manufacture
If 32-bit fixed point integer values are used for image processing, then existing library functions can be utilized, but the resolution of processable images is restricted
Solution Approach 1:
The patent segments high-resolution images into smaller tiles that fit within the precision constraints of 32-bit fixed point integers. By processing each tile at full resolution and assembling them together, the system achieves high overall image resolution while maintaining compatibility with fixed point arithmetic operations.
3Area of stationary object
If images are segmented into smaller tiles, then large and high-resolution images can be processed, but the number of segments increases processing complexity
Solution Approach 1:
The patent performs preliminary actions by determining optimal tile sizes and segmentation strategies before processing begins. This pre-planning minimizes the number of tiles required and optimizes the processing workflow, reducing overall complexity while still enabling large image processing.
Solution Approach 2:
The patent merges multiple processed tiles back together to reconstruct the complete large image. This combining step consolidates the results from individual tile processing into a unified output, managing complexity by providing a clear final assembly operation.
Data Source
AI summary
To facilitate rendering an image, e.g., a portion of a PDF page, at higher resolutions or locations than could be expressed using standard 32 bit fixed point integer values, an image is segmented and a user space coordinate system origin used for individual segment processing is transformed on a per segment basis to each segment locality before a render request is made to a rendering function library. After a new origin of a segment is established, the PDF page content contained within that locality corresponding to the individual segment being processed is rendered, e.g., using functions available in the PDF software library. The results of processing the segments is combined to produce a final high-resolution image. While the input to the rendering process may include values which can not be expressed in 32 bit fixed point notation, the segmentation process allows the individual segments to be rendered using rendering specified using such notation.