Lossless Image Compression via Block Prediction and Context Modeling
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Solution Overview
Problem
Current lossless image compression algorithms are inefficient in reducing file size and storage requirements, particularly in cloud-based environments, due to suboptimal pixel prediction and context modeling techniques.
Innovation Solution
The proposed solution involves block-based prediction and machine learning-based context modeling, where each image block is assigned a best-performing predictor and dynamically selected properties for context modeling, improving compression performance by reducing residuals and enhancing entropy coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional pixel prediction and context modeling techniques are used, then the compression algorithm is simpler to implement, but the compression performance is insufficient and file sizes remain large
Solution Approach 1:
The image is divided into multiple blocks, and each block is associated with a specific pixel predictor. This segmentation allows the system to apply different prediction strategies to different regions, improving overall compression performance while managing complexity through localized processing
Solution Approach 2:
The system dynamically selects the best-performing predictor for each block based on local image characteristics. This dynamic adaptation enables the algorithm to optimize prediction accuracy for each region without requiring a fixed complex structure across the entire image
Solution Approach 3:
The context modeling uses dynamically selected properties rather than fixed parameters. By changing the properties used for context modeling based on local image features, the system improves compression performance while maintaining reasonable algorithmic complexity
2Manufacturing precision
If larger block sizes are used for prediction, then the algorithm complexity is reduced, but the prediction error increases leading to larger residuals
Solution Approach 1:
By dividing the image into smaller blocks with dedicated predictors, the system achieves higher prediction accuracy within each block while managing complexity through the structured approach of block-based processing
Solution Approach 2:
Each block receives a predictor tailored to its local characteristics, ensuring high prediction accuracy for that specific region. This local optimization approach improves overall precision without requiring uniformly high complexity across the entire image
3Productivity
If fixed context modeling properties are used, then the algorithm is simpler to implement, but compression performance is suboptimal
Solution Approach 1:
The context modeling dynamically selects properties based on local image characteristics rather than using fixed properties throughout. This dynamic selection improves compression efficiency by adapting to local variations while maintaining manageable complexity through systematic property selection
Solution Approach 2:
The system changes the properties used for context modeling based on local image features. By adapting the modeling parameters to local conditions, the system achieves better compression performance without requiring overly complex fixed-parameter structures
Data Source
AI summary
Embodiments are disclosed for lossless image compression using block-based prediction and context adaptive entropy coding. A method of lossless image compression using block-based prediction and context adaptive entropy coding comprises dividing an input image into a plurality of blocks, determining a pixel predictor for each block based on a block strategy, determining a plurality of residual values using the pixel predictor for each block, selecting a subset of features associated with the plurality of residual values, performing context modeling on the plurality of residual values based on the subset of features to identify a plurality of residual clusters, and entropy coding the plurality of residual clusters.