Image Compression via Template Pattern Sub-Region Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image compression methods either lose data, leading to inaccuracies or require significant processing power for lossless compression, failing to efficiently represent image content while reducing data size.
Innovation Solution
Divide images into sub-regions and represent each with template patterns from a dictionary, allowing for efficient data representation by referencing these patterns, with optional error values for lossless compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression is used to reduce data size, then compression efficiency is improved, but image accuracy deteriorates
Solution Approach 1:
The image is divided into multiple sub-regions, and each sub-region is represented by a template pattern from a dictionary. This segmentation allows the system to achieve compression by representing each region with a reference pattern rather than storing all pixel data, thereby reducing data size while maintaining acceptable image quality through the template matching approach.
Solution Approach 2:
Template patterns are created as copies of representative image regions and stored in a dictionary. During compression, instead of storing the actual image data, the system stores references to these template patterns. This copying mechanism enables efficient compression while preserving image characteristics, as the templates serve as accurate representations of the original regions.
2Measurement precision
If lossless compression is used to maintain image accuracy, then image fidelity is improved, but processing power requirements worsen
Solution Approach 1:
By dividing the image into sub-regions and representing each with a template pattern, the system reduces the amount of data that needs to be processed and stored. This segmentation approach lowers processing power requirements compared to traditional lossless compression methods that must process every pixel while still maintaining image fidelity through accurate template matching.
Solution Approach 2:
The system creates template patterns as copies of representative regions and stores them in a dictionary. During compression, the system references these templates rather than processing all original pixel data, significantly reducing processing power requirements while maintaining image fidelity through the use of accurate template representations.
3Productivity
If template pattern compression is used to reduce data size, then compression efficiency is improved, but device complexity worsens
Solution Approach 1:
Template patterns are pre-computed and stored in a dictionary before the actual compression process. This preliminary action allows the compression system to simply reference existing templates during processing, rather than creating and managing complex data structures in real-time, thereby reducing operational complexity while maintaining high compression efficiency.
Data Source
AI summary
A system and method for compressing a digital image may store a dictionary having a plurality of predetermined template patterns each identified by a code. The image may be divided into a plurality of sub-regions, and a template pattern may be selected from the dictionary that most closely matches an image features for each sub-region. A compressed data set may be generated for the image in which each sub-region is represented by a code identifying the associated template pattern. Once the data is compressed, the image may be reconstructed using the code from the compressed data set to retrieve the template pattern from the dictionary for each sub-region and may reconstruct a full image by combining the template patterns for all of the sub-regions.