Camera Image Brightness Adjustment Using Tile-Based ROI Histograms
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Solution Overview
Problem
Existing image processing systems face challenges in efficiently adjusting brightness for heterogeneous images with bright and dark regions, particularly in code reading applications, due to the high computational costs associated with determining global brightness distributions.
Innovation Solution
The image is subdivided into subregions (tiles) and regions of interest (ROIs), with local brightness distributions determined for each tile, and a common brightness distribution is generated for the ROI by combining selected tile distributions, allowing for efficient local contrast spreading without re-accessing pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a global brightness distribution is determined for the entire image, then brightness adjustment can be applied uniformly, but the computational cost increases significantly due to the large number of pixels that must be accessed
Solution Approach 1:
The image is divided into multiple tiles (subregions), and brightness distributions are determined independently for each tile rather than for the entire image. This segmentation reduces the number of pixels accessed at any one time, thereby reducing computational cost while maintaining brightness adjustment accuracy within each local region.
Solution Approach 2:
Instead of applying a single global brightness distribution to the entire image, the patent determines local brightness distributions for different regions (tiles) and applies them locally. This allows each region to be adjusted according to its own characteristics, improving local brightness accuracy while reducing the computational burden of processing the entire image at once.
2Productivity
If local brightness adjustment is performed for regions of interest, then processing efficiency improves, but the complexity of identifying and processing specific regions increases
Solution Approach 1:
The patent segments the image into a regular grid of tiles, providing a simple and systematic way to divide the image without requiring complex region identification algorithms. This regular segmentation structure makes it easy to process each tile independently while maintaining overall image coverage.
Solution Approach 2:
The patent applies brightness adjustment to all tiles (excessive action) rather than only to identified regions of interest. This approach simplifies the process by avoiding the need to identify specific regions of interest, while still ensuring that important regions receive appropriate brightness adjustment. The regular tile structure ensures complete coverage without requiring complex selection criteria.
Data Source
AI summary
A camera device having an image sensor for recording an image and having a control and evaluation unit configured to subdivide the image into first subregions, to find a second subregion in the image with structures of interest, and to carry out a brightness adjustment of the image, wherein the control and evaluation unit is further configured to determine a respective brightness distribution for the first subregions, to determine a selection of those first subregions that correspond to the second subregion, to generate a common brightness distribution of the second subregion from the brightness distributions of the first subregions of the selection, and to use the common brightness distribution to carry out the brightness adjustment for the second subregion.