Raster Image Segmentation for Mixed Content Compression
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Solution Overview
Problem
Current image compression methods, such as JPEG, often result in poor user experience due to text smudging and inefficient compression of documents with mixed tone and image content, especially over slow internet connections, requiring additional software and leading to long download times for PDF documents.
Innovation Solution
The method segments images into blocks based on color palettes and employs different compression algorithms (lossy or lossless) suited to each section, allowing for optimized display formats and storage, either as individual files or within a single file, using standard formats like HTML, which enables faster download and improved readability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single compression algorithm (e.g., JPEG) is used for the entire image, then the compression process is simple, but text quality deteriorates due to smudging effects
Solution Approach 1:
The image is divided into multiple sections, each processed with an appropriate compression algorithm. Text-heavy sections use lossless compression to preserve sharp edges, while photographic sections use lossy compression to achieve better compression ratios. This segmentation resolves the contradiction by allowing different compression strategies for different content types within the same image.
Solution Approach 2:
Different compression qualities are applied to different regions of the image based on content type. Text regions receive lossless compression maintaining high quality, while image regions accept lossy compression for better compression efficiency. This local differentiation resolves the contradiction between simplicity and text quality preservation.
2Manufacturing precision
If PDF format is used for document compression, then compression quality is maintained, but download time increases and additional software is required
Solution Approach 1:
The document is divided into image sections that are compressed and stored individually or in small groups. This segmentation allows selective loading of only visible portions, significantly reducing download time while maintaining quality where needed. The segmentation also enables parallel loading and caching strategies.
Solution Approach 2:
The patent changes the format parameter from traditional PDF to segmented image formats (such as PNG or JPEG) with associated metadata. This parameter change enables browser-native rendering without additional software, while the segmentation strategy maintains compression quality by applying appropriate algorithms to different regions.
3Productivity
If JPEG compression is used for text-containing images, then compression ratio improves, but text readability deteriorates due to smudging
Solution Approach 1:
The image is segmented into text regions and image regions. Text regions are processed with lossless compression algorithms that preserve sharp edges and readability, while image regions use JPEG compression for better compression ratios. This segmentation resolves the contradiction by applying the right algorithm to the right content type.
Solution Approach 2:
Different compression qualities are applied locally to different parts of the image. Text areas receive lossless compression maintaining high readability, while photographic areas accept lossy compression for improved compression ratio. This local quality differentiation resolves the contradiction between compression efficiency and text readability.
4Manufacturing precision
If lossless compression is used for entire image, then text quality is preserved, but compression efficiency decreases for photographic content
Solution Approach 1:
The image is divided into sections with different compression requirements. Text sections use lossless compression to preserve quality, while photographic sections use lossy compression to achieve better compression efficiency. This segmentation allows the system to optimize for both quality and efficiency simultaneously in different regions.
Solution Approach 2:
Different compression strategies are applied to different regions based on content type. Text regions maintain lossless compression for quality preservation, while image regions use lossy compression for efficiency. This local differentiation resolves the contradiction between text quality preservation and overall compression efficiency.
Data Source
AI summary
The invention relates to a method for the compression of a raster type image material by using segmentation, such that: a widespread start image (1) is divided into segments (1A, 1B, 1C, 1D), such that the segments contain distinct image sections different from each other at least in terms of color qualities thereof, and each segment is compressed with an image compression algorithm chosen according to internal qualities of the segment or with a different set of parameters for the image compression algorithm, coordinates for each segment portion are stored in a memory, and compressed segments (A, B, C, D) and coordinates (XA, XB, XC, XD) therefore are processed for a compression result R.