Image Compression Edge Extraction for Text Visibility
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Solution Overview
Problem
Existing image compression techniques face challenges in accurately separating text from background layers, leading to reduced visibility of text during compression, especially when text is incorrectly identified as background and subjected to lossy compression.
Innovation Solution
An image compressing apparatus that allows users to select compression ratios and processes for improving the visibility of black and color text, including edge extraction and layer separation, enabling reliable text separation and improved file formats like PDF or XPS for enhanced text visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression technique is used on the background layer, then the compression ratio is easily controlled and file size is reduced, but the image quality and text visibility are degraded
Solution Approach 1:
The image is segmented into text regions and background regions using foreground mask generation. The text regions are extracted and processed separately from the background, allowing lossless compression for text (maintaining visibility) and lossy compression for background (achieving high compression ratio). This segmentation resolves the contradiction by applying different compression strategies to different parts of the image.
Solution Approach 2:
Different compression quality levels are applied to different regions of the image. Text regions are compressed with lossless techniques (JBIG, MMR) to preserve visibility, while background regions are compressed with lossy techniques (JPEG) to achieve higher compression ratios. This local differentiation allows simultaneous optimization of both text visibility and overall compression ratio.
2Manufacturing precision
If the entire text shape is extracted to improve text visibility, then text readability is maintained, but various pre-processes and post-processes are required to exclude non-text elements, increasing processing complexity
Solution Approach 1:
The invention extracts only the essential text edge pixels (one-pixel-wide edges) rather than the entire text shape. This extraction approach maintains text visibility while significantly reducing the need for complex pre-processing and post-processing steps to exclude non-text elements like photograph edges, halftone dots, and noise.
Solution Approach 2:
Instead of extracting the complete text shape, the invention uses partial extraction by capturing only the edge pixels that define text boundaries. This partial action is sufficient to maintain text visibility and readability while avoiding the complexity of extracting and processing the entire text area.
3Manufacturing precision
If black text edge pixels are extracted and compressed separately, then black text visibility is improved, but the device complexity increases due to additional processing steps
Solution Approach 1:
The compression process is segmented into distinct stages: black text edge extraction, color text edge extraction, background processing, and layered compression. Each stage handles specific tasks independently, making the overall complex process more manageable and allowing optimization of each segment without affecting others.
Solution Approach 2:
The invention provides dynamic and flexible processing where the extraction and compression of black text edges can be selectively applied based on image characteristics and compression requirements. The system can adaptively adjust the processing intensity and apply different compression ratios to different text types (black vs. color text).
Data Source
AI summary
A color image processing apparatus is provided with a black text emphasis mode, in which, when compressing an image in a compression processing section, edges of a black text in the image are detected, a foreground layer in which the detected edges of the black text are represented by a binary image is generated, and lossless compression is performed on the foreground layer. When the black text emphasis mode is selected, a background layer in which the difference in density between the edges of the black text and pixels in the vicinity of the edges in the image is reduced is generated, and lossy compression is performed on the background layer. With this operation, in an image obtained by decompressing a compressed file, outline of the black text is clearly represented, and an artifact caused by the lossy compression is suppressed.