Adaptive Pixel Prediction for Medical Image Compression
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Solution Overview
Problem
Current prediction models for image compression, particularly in medical imaging, fail to accurately predict pixel values in images with edge characteristics, leading to inefficiencies in data compression and increased data rates, especially in lossless compression scenarios.
Innovation Solution
The method involves selecting reference pixels from an image to generate predicted pixel intensities using adaptive model functions that represent the intensity characteristics of the reference pixels, allowing for more precise predictions and improved compression ratios through entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If current prediction models are used for image compression, then compression algorithms can reduce bit rate through reversible data transforms, but prediction accuracy fails for images with edge characteristics leading to increased data rates
Solution Approach 1:
The patent applies dynamics by making the prediction model adaptive rather than static. The system dynamically adjusts prediction parameters based on local image characteristics, allowing the predictor to adapt to different regions (smooth areas vs. edge regions). This is achieved through adaptive parameter selection and context-based prediction adjustments that respond to local image content.
Solution Approach 2:
The patent implements local quality by applying different prediction strategies to different regions of the image. Instead of using a uniform prediction model, the system identifies edge regions versus smooth regions and applies appropriate prediction methods to each, optimizing prediction accuracy for local image characteristics while maintaining overall compression efficiency.
2Device complexity
If simple linear prediction from neighborhoods is used, then the method is computationally simple, but prediction precision is insufficient for images with edge characteristics
Solution Approach 1:
The patent applies segmentation by dividing the image into different regions based on local characteristics (edge regions vs. smooth regions). The system segments the prediction process into multiple paths, applying appropriate prediction models to each segment, thereby improving accuracy without requiring complex global processing.
Solution Approach 2:
The patent makes the prediction method dynamic by adaptively selecting between different prediction strategies based on local image characteristics. The system dynamically adjusts prediction parameters and model selection based on detected features, transforming a static simple predictor into an adaptive system that maintains low complexity while improving accuracy.
3Measurement precision
If non-linear predictions with image structure incorporation are used, then prediction accuracy improves for transient signals, but algorithm complexity increases
Solution Approach 1:
The patent applies local quality by applying non-linear prediction methods selectively only where needed (in edge regions and transient signal areas) rather than uniformly across the entire image. This allows the system to gain accuracy benefits where they are most needed while avoiding unnecessary complexity in smooth regions where simple linear prediction suffices.
Solution Approach 2:
The patent segments the image processing into different regions requiring different prediction approaches. By identifying and separating edge regions from smooth regions, the system applies complex non-linear prediction only to the necessary segments, reducing overall algorithmic complexity while maintaining high accuracy where required.
4Reliability
If lossless compression is demanded for medical images, then diagnostic accuracy is preserved, but compression ratio is reduced compared to lossy compression
Solution Approach 1:
The patent applies dynamics by implementing an adaptive prediction system that optimizes compression for lossless medical imaging. The adaptive nature of the predictor allows it to achieve better compression ratios by accurately modeling local image characteristics, reducing the entropy of the residual data while maintaining the exact original image quality required for diagnostic purposes.
Data Source
AI summary
The embodiments relate to pixel-prediction methods and devices that are based on one of a selection of one out of at least two model functions that provide a prediction function and an adaptive model function that is capable to predict intensity characteristics of reference pixels used for prediction.