Adaptive Pixel Compression Using Adjacent Similarity Analysis
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Solution Overview
Problem
Existing pixel data compression methods for display devices result in data loss and image deterioration due to limited memory capacity, leading to increased manufacturing costs and reduced image quality.
Innovation Solution
A method that calculates the similarity between adjacent pixels and selects an appropriate compression mode to minimize data loss, using either a first compression mode with a higher difference in compression ratios or a second mode with a lower difference, allowing for efficient compression and decompression of pixel data while maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pixel data is truncated or compressed to reduce memory size, then manufacturing costs are reduced, but image quality deteriorates due to data loss and distortion
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting compression parameters based on the calculated similarity between adjacent pixels. When similarity is high, more aggressive compression is applied; when similarity is low, less compression is used to preserve image quality. This resolves the contradiction by making compression intensity adaptive rather than fixed.
Solution Approach 2:
The patent introduces dynamics by selecting different compression modes based on real-time analysis of pixel similarity. The system transitions between different compression strategies (e.g., differential compression vs. direct compression) depending on the local image characteristics, allowing optimal balance between memory reduction and quality preservation in different regions.
2Quantity of substance
If uniform compression is applied to all pixel data, then memory size is reduced, but image quality deteriorates due to unnecessary compression of similar pixels
Solution Approach 1:
The patent applies local quality by treating different regions of the image differently based on their local characteristics. Regions with high pixel similarity undergo more aggressive compression, while regions with low similarity retain more data. This spatially varying compression strategy optimizes the balance between memory usage and quality preservation locally rather than applying a global compression rate.
Solution Approach 2:
The patent segments the image processing into different compression modes that are selectively applied to different pixel pairs based on their similarity. By dividing the compression task into multiple strategies (differential compression, direct compression, etc.) and selecting the appropriate one for each region, the system achieves efficient memory usage without uniformly degrading image quality.
3Device complexity
If simple compression is used to reduce complexity, then processing speed increases, but image quality deteriorates due to insufficient compression algorithms
Solution Approach 1:
The patent introduces dynamics by selecting between different compression algorithms based on pixel similarity calculations. The system dynamically chooses from multiple compression strategies (differential compression, direct compression) rather than using a single fixed algorithm, allowing it to adapt to local image characteristics and maintain quality while managing complexity.
Solution Approach 2:
The patent applies parameter changes by adjusting the compression approach based on the similarity parameter between adjacent pixels. When similarity exceeds a threshold, differential compression is used; otherwise, direct compression is applied. This parameter-based selection allows the system to optimize between complexity and quality based on actual image content.
Data Source
AI summary
A pixel data compression/decompression system, medium, and method, including determining the similarity between a first pixel data and a second pixel data adjacent to the first pixel data, selecting one of a first compression mode, where a difference between a compression ratio of the first pixel data and a compression ratio of the second pixel data is high, and a second compression mode, where a difference between a compression ratio of the first pixel data and a compression ratio of the second pixel data is low, based on the similarity, and compressing the first pixel data and the second pixel data based on the selected compression mode.