Optical Sensor Image Encoding With Region-Specific Code Rules
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Solution Overview
Problem
Existing image processing systems face challenges in efficiently storing or processing images with varying levels of detail using minimal storage capacity while maintaining high resolution, often requiring excessive memory due to uniform encoding methods.
Innovation Solution
A method for generating an encoding rule based on the frequency distribution of light signal values in an image, assigning code words to these values to optimize storage efficiency, using different code word spaces for regions with varying levels of detail, and employing multiple encoding rules for different image segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a very high image resolution is used to reproduce details accurately, then the image quality and detail reproduction are improved, but the storage capacity and memory requirements increase enormously
Solution Approach 1:
The patent applies local quality by using different encoding rules for different regions of the image. High-detail regions are encoded with higher precision while low-detail regions use lower precision encoding, allowing accurate detail reproduction where needed while reducing storage requirements in areas where it is not necessary.
Solution Approach 2:
The patent changes the encoding parameters dynamically based on the local characteristics of image regions. By analyzing the frequency distribution of light signal values and determining detail levels, the system adjusts the number of code words and encoding precision locally, thereby optimizing the balance between image quality and storage capacity.
2Device complexity
If uniform encoding is used across the entire image, then the encoding process is simple and consistent, but it requires excessive memory for regions that contain fewer details
Solution Approach 1:
The patent segments the image into different regions based on their detail characteristics. By dividing the image into high-detail and low-detail regions and applying different encoding rules to each segment, the system reduces overall memory requirements while maintaining encoding consistency within each segment.
Solution Approach 2:
The patent applies local quality by using different encoding rules for different regions of the image. High-detail regions are encoded with higher precision while low-detail regions use lower precision encoding, allowing accurate detail reproduction where needed while reducing storage requirements in areas where it is not necessary.
3Measurement precision
If more code words are used to encode regions with many distinguishable objects, then the encoding accuracy and object distinction capability are improved, but the storage space required increases
Solution Approach 1:
The patent changes the encoding parameters dynamically based on the local characteristics of image regions. By analyzing the frequency distribution of light signal values and determining detail levels, the system adjusts the number of code words and encoding precision locally, thereby optimizing the balance between image quality and storage capacity.
Data Source
AI summary
A method for generating at least one encoding rule to encode an image captured by an optical sensor. The method includes at least one step of reading in the image captured by the optical sensor, and a step of generating a frequency distribution of an occurrence of light-signal values at different pixels in the image. The method further includes a step of assigning code words to light-signal values, using the frequency distribution, in order to generate the at least one encoding rule for encoding the image captured by the optical sensor.