Image Sensor Module Adaptive Compression for Isolated Regions
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Solution Overview
Problem
Current image sensors face challenges in efficiently compressing data from isolated regions of images, leading to increased data loss and reduced compression efficiency due to the large differences in pixel values between target and reference pixels.
Innovation Solution
An image sensor module and method that employs a hybrid encoding approach, using differential pulse code modulation (DPCM) and horizontal or vertical balance encoding to compress pixel data, generating average values and balance information to create bitstreams that minimize data loss and enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression methods are used on isolated regions, then compression speed is maintained, but compression efficiency decreases and data loss increases
Solution Approach 1:
The patent applies different compression strategies to different regions of the image. For isolated regions (where surrounding pixels have significantly different values), the system uses horizontal/vertical balance encoding that exploits spatial correlation within the isolated region itself. For non-isolated regions, conventional DPCM methods are used. This local adaptation of compression quality resolves the contradiction by optimizing for data loss reduction specifically where needed (isolated regions) without compromising overall compression efficiency.
Solution Approach 2:
The patent dynamically selects between different compression methods (DPCM vs. horizontal/vertical balance encoding) based on the local characteristics of each pixel group. The system calculates isolation metrics and adapts the compression approach in real-time for each region, allowing it to switch between methods to optimize both compression efficiency and data loss prevention depending on the local image structure.
2Productivity
If conventional DPCM compression is used, then compression speed is maintained, but compression ratio and efficiency are reduced for isolated regions
Solution Approach 1:
The patent segments the image processing into distinct stages: first identifying isolated regions using isolation metrics, then applying appropriate compression methods to each segment. This segmentation allows the system to apply the more complex horizontal/vertical balance encoding only where necessary (in isolated regions) while using simpler DPCM elsewhere, thereby improving overall compression ratio without excessively increasing average device complexity.
Solution Approach 2:
The patent performs preliminary analysis to identify isolated regions before applying compression. By pre-calculating isolation metrics and determining which regions require special handling, the system prepares the data structure in advance, making the subsequent compression process more efficient and reducing the actual encoding complexity during the compression phase itself.
3Loss of information
If horizontal or vertical balance encoding is applied to all regions, then data loss is reduced, but processing time and computational load increase
Solution Approach 1:
The patent applies horizontal/vertical balance encoding with compensation values only to isolated regions where it is most beneficial for reducing compression loss. For non-isolated regions, the system uses the faster conventional DPCM method. This localized application resolves the contradiction by minimizing compression loss specifically where spatial correlation is weakest (in isolated regions) while maintaining fast processing elsewhere.
Solution Approach 2:
The patent changes the compression parameter (encoding method) based on the isolation parameter of each region. By monitoring the isolation metric and dynamically adjusting which compression method is applied, the system optimizes the balance between compression loss reduction and processing time, applying the more computationally intensive method only when the isolation parameter indicates it will be beneficial.
Data Source
AI summary
Provided are an image sensor module, an image processing system, and an image compression method. The image compression method of compressing image data generated by an image sensor includes: receiving pixel values of a target pixel group of image data on which compression is to be performed, and reference values of reference pixels to be used in compression of the target pixel group; determining an averaging direction in which an averaging calculation is to be performed on target pixel values; averaging the pixel values of target pixels in the averaging direction; generating balance information including compensation values to be applied to the average values based on the reference pixels; and generating a bitstream based on the average values, the balance information, and compression information.