Image Data Contrast Enhancement and Color Mapping for Compression
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Solution Overview
Problem
Existing data compression algorithms, such as those used in the GIF format, struggle to accommodate different types of data effectively, leading to reduced compression ratios and increased data transmission times, especially for image-intensive documents.
Innovation Solution
The system processes image data before compression by enhancing data contrasts and applying custom color mapping based on dynamically selected parameters like bandwidth, zoom level, and desired image size, using tonal reproduction curves to increase correlated areas and reduce color levels, thereby enhancing data compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If standard GIF compression is used for image-intensive documents, then the data can be transmitted with basic compression, but the compressed data size becomes much larger (e.g., 30 kB for 240×320 pixels)
Solution Approach 1:
The patent applies contrast enhancement and color mapping transformations to the image data before compression. This preliminary processing creates larger correlated areas in the data, which significantly improves the subsequent compression ratio. For example, contrast enhancement transforms pixel values to create larger uniform regions, and color mapping reduces the color space to create more predictable patterns that compress better.
Solution Approach 2:
The patent dynamically changes multiple parameters including contrast enhancement strength, color mapping tables, and tonal reproduction curves based on the image content and transmission requirements. These parameter changes optimize the data for compression by creating more predictable patterns and reducing entropy in the image data before compression is applied.
2Productivity
If contrast enhancement and custom color mapping are applied before compression, then compression ratio increases significantly, but additional processing steps are required
Solution Approach 1:
The patent implements a universal processing framework that handles multiple image types (scanned documents, photographs, text-heavy images) through the same contrast enhancement and color mapping pipeline. The system dynamically selects parameters based on image analysis, making the complex processing adaptable to different content types without requiring separate processing paths for each image category.
Solution Approach 2:
The system automatically analyzes image content and dynamically selects optimal processing parameters without manual intervention. The contrast enhancement and color mapping parameters are self-adjusted based on the image characteristics, reducing the need for manual configuration while maintaining optimal compression ratios across different image types.
3Speed
If dynamic parameter selection is used based on bandwidth and zoom level, then transmission efficiency is optimized, but the system requires more complex parameter management
Solution Approach 1:
The patent implements dynamic parameter selection that adapts to real-time conditions including network bandwidth, display zoom level, and image content characteristics. The system continuously adjusts contrast enhancement strength, color mapping parameters, and compression settings based on these dynamic factors, optimizing transmission speed and quality for each specific scenario.
Solution Approach 2:
The system uses feedback from image analysis and transmission conditions to automatically adjust processing parameters. By analyzing the image content and transmission requirements, the system selects optimal parameters for contrast enhancement and color mapping, creating a closed-loop system that optimizes performance based on actual conditions rather than fixed settings.
Data Source
AI summary
Systems and methods provide data processing before data compression. The data processing includes contrast enhancement and/or custom color mapping.