Adaptive Image Compression Using Metadata-Driven Bit Manipulation
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Solution Overview
Problem
Conventional compression/decompression systems fail to effectively compress noisy image data while preserving image quality, particularly struggling with continuous-tone pixel maps using lossless methods and text using lossy methods.
Innovation Solution
An adaptive lossless compression system that determines the type of image data through meta data, modifies pixels by setting specific bits to zero, and uses mechanisms like LZW or LZA for lossless compression, ensuring efficient storage and rendering of both noisy and text-based image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression methods are used, then image quality is preserved, but compression effectiveness on noisy image data is poor
Solution Approach 1:
The patent applies different compression strategies to different regions of image data based on their characteristics. Text regions are compressed using one method while noisy continuous-tone regions are compressed using another method, allowing each region to be optimized for its specific properties rather than applying a uniform compression approach throughout the entire image.
Solution Approach 2:
The compression system dynamically adapts its behavior based on the detected characteristics of the image data. By analyzing metadata and pixel properties, the system switches between different compression algorithms (lossless for text, lossy for noisy continuous-tone images) to optimize both compression ratio and quality preservation in real-time.
2Productivity
If lossy compression methods are used, then compression effectiveness on noisy image data is good, but image quality on text is degraded
Solution Approach 1:
The system identifies different regions within the image data and applies appropriate compression methods to each. Text regions maintain their original quality using lossless compression, while noisy continuous-tone regions benefit from lossy compression, thus achieving both high compression ratios and quality preservation where needed.
Solution Approach 2:
The compression process dynamically selects between lossless and lossy methods based on the detected type of image data. The system analyzes metadata and pixel characteristics to determine whether to apply lossless compression for text or lossy compression for noisy images, optimizing the balance between compression efficiency and quality preservation.
3Device complexity
If a single compression method is used, then system simplicity is maintained, but adaptability to different image data types is poor
Solution Approach 1:
The compression system incorporates dynamic detection and adaptation capabilities that analyze image data characteristics in real-time. By examining metadata and pixel properties, the system automatically determines the appropriate compression method (lossless or lossy) and switches between algorithms as needed, providing adaptability without requiring manual intervention.
Solution Approach 2:
The system changes its operational parameters based on detected image characteristics. By monitoring metadata and pixel properties, the compression system adjusts its behavior to match the specific requirements of different image types, switching between different compression algorithms and parameter sets to optimize performance for each data type.
Data Source
AI summary
A system and method adaptively lossy compresses image data by receiving a pixel of image data and meta data indicating a type of object that generated the pixel of image data, the pixel of image data includes a first byte having most significant bits of image data and a second byte having least significant bits of image data; electronically determining if the meta data associated with the pixel of image data is associated with a noisy image or indicates that the pixel of image data was generated by a specific type of object; electronically modifying the pixel of image data, when it is determined that the meta data is associated with the pixel of image data is associated with a noisy image or indicates that the pixel of image data was generated by a specific type of object, by setting a predetermined number of low bits of the pixel of image data to zero.