Actively-Learned Context Modeling for Efficient Image Compression
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Solution Overview
Problem
Conventional image compression systems face significant computational and resource challenges due to the resource-intensive process of building context models using all available context-residual pairs, which is a critical but time-consuming step in lossless image compression.
Innovation Solution
Actively-learned context modeling selectively identifies a subset of data to build a context model, iteratively updating it based on likelihood of prediction or entropy values to achieve similar performance to a model built with all data, reducing computational burden and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a context model is built using all available context-residual pairs, then the compression accuracy and reliability are improved, but the computational time and resource consumption increase significantly
Solution Approach 1:
The patent extracts only the most informative subset of context-residual pairs from the complete dataset to build the context model. By identifying and using only the critical data points that provide the highest predictive value, the system achieves accurate compression models without processing all available data, thus reducing computational time while maintaining reliability.
Solution Approach 2:
The patent changes the parameter of data quantity from using all context-residual pairs to using a selectively chosen subset. Through parameter optimization, the system determines the optimal subset size and selection criteria that maintain compression accuracy while significantly reducing the computational burden and time required for context model building.
2Reliability
If a context model is built using all available context-residual pairs, then the compression accuracy is improved, but the computational resources and processing complexity increase
Solution Approach 1:
The patent extracts and processes only the essential subset of context-residual pairs rather than handling the complete dataset. This extraction approach reduces the computational complexity and processing requirements while maintaining the accuracy needed for effective image compression.
Solution Approach 2:
The patent segments the complete set of context-residual pairs into a manageable subset that contains the most informative data points. By dividing the large dataset into a smaller, strategically selected portion, the system reduces computational complexity while preserving the necessary information for accurate compression modeling.
3Loss of time
If a subset of data is used to build the context model, then the computational time and resources are reduced, but the compression accuracy may degrade
Solution Approach 1:
The patent employs feedback mechanisms to iteratively refine the subset selection process. By evaluating the performance of context models built from different subsets and using this feedback to guide further selections, the system ensures that the chosen subset maintains sufficient accuracy for compression while minimizing computational time.
Solution Approach 2:
The patent optimizes parameters such as subset size, selection criteria, and sampling strategies to maintain compression accuracy while reducing computational time. Through careful parameter tuning, the system achieves the optimal balance between model accuracy and processing efficiency.
Data Source
AI summary
Embodiments described herein provide methods and systems for facilitating actively-learned context modeling. In one embodiment, a subset of data is selected from a training dataset corresponding with an image to be compressed, the subset of data corresponding with a subset of data of pixels of the image. A context model is generated using the selected subset of data. The context model is generally in the form of a decision tree having a set of leaf nodes. Entropy values corresponding with each leaf node of the set of leaf nodes are determined. Each entropy value indicates an extent of diversity of context associated with the corresponding leaf node. Additional data from the training dataset is selected based on the entropy values corresponding with the leaf nodes. The updated subset of data is used to generate an updated context model for use in performing compression of the image.