Text Compression Clustering Pixel Distribution Cloud Desktop
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Solution Overview
Problem
Existing text compression methods do not optimize compression efficiency and can affect the clarity of text information during transmission in cloud desktop scenarios.
Innovation Solution
A method that clusters text blocks into areas based on pixel distribution, acquires text row distribution information, scans each text row to gather original pixel information, and performs lossless compression on this information to improve compression efficiency and clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossless or near lossless compression algorithms are used to compress text areas as a whole, then the compression efficiency is improved, but the clarity of text information after transmission deteriorates
Solution Approach 1:
The patent segments text areas into text rows and further into text characters, applying compression at multiple levels. By processing text rows individually and then compressing character-level data, the method achieves both high compression efficiency and maintained text clarity, resolving the contradiction between compression rate and quality preservation.
Solution Approach 2:
The patent applies different compression strategies to different parts of the text data. Text rows are processed to extract distribution characteristics, and then character-level compression is applied locally to each text row based on its specific distribution properties. This localized approach allows optimization of both compression efficiency and clarity for each region.
2Speed
If text areas are compressed as a whole using existing compression algorithms, then the processing speed is improved, but the compression efficiency for text-specific characteristics deteriorates
Solution Approach 1:
The patent divides the text processing into distinct stages: first segmenting text areas into text rows, then further segmenting into text characters. This segmentation enables the application of text-specific compression algorithms that leverage character distribution characteristics, achieving higher compression efficiency while maintaining processing speed through structured processing.
Solution Approach 2:
The patent performs preliminary processing to extract text row distribution characteristics before applying compression. By pre-analyzing the distribution of characters in each text row and storing this information, the subsequent compression process can directly utilize these characteristics, improving compression efficiency without significant speed penalty.
Data Source
AI summary
A method for information compression/decompression, apparatuses and a non-transitory computer-readable storage medium are disclosed. The method for information compression may include: clustering text blocks to be processed into respective-text areas according to pixel distribution information of the text blocks to be processed; acquiring text row distribution information of each of the text areas according to foreground pixels of each text row in each of the text areas; scanning each text row in each of the text areas according to the acquired text row distribution information to acquire original pixel information of each text row; and performing lossless compression on the text row distribution information of a plurality of the text areas and the original pixel information of each text row of the plurality of the text areas.