Hierarchical Block Decompression for Few-Colored Image Storage
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Solution Overview
Problem
Existing lossless image compression techniques are not optimized for compressing few-colored images, such as those with 1024 or fewer colors, leading to suboptimal compression and inefficiencies in storage and transmission.
Innovation Solution
A hierarchical lossless compression method that decomposes images into fixed-size blocks, recursively divides multi-colored blocks into smaller sub-blocks, and entropy codes the block information, allowing for efficient storage and progressive decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing lossless image compression techniques (GIF, PNG) are used on few-colored images, then the images can be compressed and stored, but the compression efficiency is suboptimal and storage space is wasted
Solution Approach 1:
The image is divided into multiple blocks, and each block is independently analyzed to determine whether it is single-colored or multi-colored. This segmentation allows the compression algorithm to apply different encoding strategies to different regions, optimizing compression efficiency for few-colored images while reducing storage space requirements
Solution Approach 2:
Different encoding methods are applied to different blocks based on their color characteristics. Single-colored blocks use more aggressive compression while multi-colored blocks use different encoding strategies. This local differentiation optimizes overall compression efficiency and reduces wasted storage space
2Quantity of substance
If hierarchical block decomposition is applied to few-colored images, then compression efficiency improves and storage space reduces, but the processing complexity increases
Solution Approach 1:
The image processing is segmented into hierarchical blocks at different levels. This segmentation breaks down the complex processing task into manageable sub-tasks, where each block can be independently analyzed and encoded, reducing overall processing complexity while achieving efficient compression
Solution Approach 2:
The algorithm dynamically adjusts block sizes and decomposition levels based on image characteristics. By changing parameters such as block dimensions and recursion depth, the system optimizes the balance between compression efficiency and processing complexity for different few-colored images
3Productivity
If recursive decomposition of multi-colored blocks is performed, then compression efficiency increases, but the computational time and processing duration increase
Solution Approach 1:
The recursive decomposition is applied selectively rather than uniformly to all blocks. Single-colored blocks are identified and compressed without further decomposition, while only multi-colored blocks undergo recursive decomposition. This partial application reduces processing time while maintaining high compression efficiency for the regions that benefit most
Data Source
AI summary
Architecture that utilizes characteristics of high spatial redundancy and few colors in few-colored images to compress the image data in a hierarchical manner, and store block level information. The entire image is decomposed into fixed-size blocks, and the block color information (single-colored or multi-colored) is stored. All multi-colored blocks are further divided into smaller blocks (sub-blocks) in a lower level, and the sub-block information is stored as well. The process is performed recursively until there are no more multi-colored blocks, or until the block size reaches a minimum size (e.g., a 1×1 block or one pixel). The information is then entropy coded.