Adaptive Lossless Image Compression via Segmented Mapping Tables
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Solution Overview
Problem
Current data compression methods for digital image data in digital imaging applications are inefficient, leading to increased system requirements for storage, processing, and transmission due to the large quantity of data required to maintain high fidelity images with accurate colors and brightness.
Innovation Solution
An adaptive and lossless data compression/decompression method that processes pixel data by dividing it into sections, using a mapping table to store unique data states and compressing each section independently, allowing for on-the-fly compression and decompression while maintaining data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression is applied to maintain data integrity, then data quality is preserved, but data size reduction is limited compared to lossy methods
Solution Approach 1:
The patent divides the image data into multiple data sections, with each section processed independently through its own mapping table. This segmentation allows the system to achieve better compression ratios by capturing local patterns in each section while maintaining lossless reconstruction capability through section-specific mapping tables.
Solution Approach 2:
The patent dynamically adjusts the number of entries in mapping tables based on the complexity and characteristics of each data section. By changing the parameter of mapping table size adaptively, the system optimizes compression efficiency for different types of image data while ensuring lossless reconstruction is always possible.
2Productivity
If larger mapping tables with more entries are used to improve compression ratio, then more data states can be represented, but memory requirements and processing complexity increase
Solution Approach 1:
Instead of using one large mapping table for all image data, the patent divides the data into multiple sections, each with its own smaller mapping table. This segmentation reduces the memory footprint of individual mapping tables while achieving comparable or better overall compression by capturing local data patterns more effectively.
Solution Approach 2:
The patent applies different mapping table configurations to different data sections based on their local characteristics. Each section's mapping table is optimized for its specific data patterns, allowing the system to achieve high compression ratios without requiring uniformly large mapping tables across the entire image.
3Productivity
If adaptive compression is applied to optimize for different data patterns, then compression efficiency improves, but processing complexity increases
Solution Approach 1:
The patent performs preliminary analysis of each data section to determine its characteristics before compression. This preliminary action includes assessing the data pattern complexity and configuring the mapping table accordingly, which simplifies the subsequent compression process and improves overall efficiency by avoiding unnecessary complex processing for simple data patterns.
Solution Approach 2:
The patent dynamically configures mapping tables based on the characteristics of each data section. The number of entries and structure of mapping tables are adjusted adaptively according to the local data patterns, allowing the system to optimize compression efficiency for each section while keeping processing complexity manageable through localized adaptation rather than global optimization.
Data Source
AI summary
An image compression and decompression method compresses data based upon the data states, and decompresses the compressed data based upon the codes generated during the compression.