Sensor Device Rotational Filter for Bayer Pattern Convolution
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Solution Overview
Problem
Existing image data processing techniques, particularly in convolution processing, face challenges in efficiently handling limited buffer spaces and varying image patterns, such as the Bayer pattern, which affects the effectiveness of image processing and recognition systems.
Innovation Solution
A sensor device is designed to perform convolution processing and quantization on image data with a limited buffer space by using a rotational filter that adapts to the changing storage pattern of the image data, ensuring effective processing even with image data of a Bayer pattern.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If convolution processing is performed on image data with a limited buffer space, then processing efficiency is improved, but the ability to handle varying image patterns (such as Bayer pattern) deteriorates
Solution Approach 1:
The patent applies the dynamics principle by making the filter pattern changeable according to the storage pattern of image data in the buffer. The filter selection is dynamically adjusted based on whether the buffer stores image data in a first pattern (e.g., Bayer pattern) or a second pattern (e.g., processed pattern), enabling the system to adapt to different storage patterns while maintaining processing efficiency within limited buffer constraints.
2Device complexity
If a fixed filter pattern is used for convolution processing, then device complexity is reduced, but adaptability to different image data storage patterns deteriorates
Solution Approach 1:
The system dynamically selects between a first filter pattern and a second filter pattern based on the storage pattern detection mechanism. This dynamic adaptation allows the device to handle different image data storage patterns without requiring a complex filter bank for every possible pattern, thus balancing adaptability with reasonable device complexity.
Solution Approach 2:
The system performs self-adaptation by automatically detecting the storage pattern of image data in the buffer and selecting the appropriate filter pattern without external intervention. This self-service mechanism enables the convolution processing to adapt to different storage patterns while keeping the control logic relatively simple.
Data Source
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AI summary
Disclosed are an image data processing method and a sensor device performing the same. The sensor device includes an image sensor configured to acquire image data, an image buffer configured to store the image data, and an image processor configured to generate image-processed data by applying a filter corresponding to a storage pattern of the image buffer to the image data stored in the image buffer.