Sensor-Level Image Compression Using Local Entropy Regions
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Solution Overview
Problem
High-quality image capture increases data requirements for storage and transmission, leading to higher bandwidth needs and potentially negative user experiences due to increased data volume.
Innovation Solution
A method and system for lossy compression at the sensor level, where local entropy is used to determine if features are dense or sparse, applying averaging operations to sparse regions and standard processing to dense regions, reducing data transmission requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-quality image capture is performed with greater pixel density, then image quality is improved, but storage requirements and transmission bandwidth requirements increase
Solution Approach 1:
The patent applies different processing operations to different regions of the image based on local feature density. High-entropy regions (with significant features) are processed with one operation while low-entropy regions (with minimal features) are processed with a different operation, optimizing the balance between quality and data reduction
Solution Approach 2:
The patent changes processing parameters dynamically based on local entropy calculations. The entropy value serves as a parameter that determines which pre-processing operation to apply, allowing adaptive adjustment of processing intensity to match local image characteristics
2Measurement precision
If greater pixel density is used for image capture, then image quality is improved, but transmission bandwidth requirements increase
Solution Approach 1:
The patent applies different processing operations to different regions of the image based on local feature density. High-entropy regions (with significant features) are processed with one operation while low-entropy regions (with minimal features) are processed with a different operation, optimizing the balance between quality and data reduction
Solution Approach 2:
The patent performs pre-processing operations at the sensor level before data transmission. By calculating local entropy and applying appropriate pre-processing (such as averaging in low-entropy regions) beforehand, the system reduces the data volume that needs to be transmitted, improving transmission efficiency
Data Source
AI summary
A device, system, and method perform lossy compression for images and signals by identifying regions with a low density of features. The method performed at a sensor communicatively connected to a receiver includes capturing sensor data. The method includes selecting a position in the sensor data. The method includes determining a local entropy of the position based on an entropy operation that indicates a probability distribution of a plurality of available values. When the local entropy is below a predetermined threshold, the method includes applying a first pre-processing operation to the position that averages features included in the position. The method includes transmitting the pre-processed sensor data corresponding to the position to the receiver.