Image Processing Apparatus for Embedded Code Decoding
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Solution Overview
Problem
Existing image processing methods fail to precisely extract embedded codes due to 'dead regions' caused by non-linearity in input devices, leading to incorrect decoding when a large number of block pairs have flat average pixel values, resulting in errors that exceed error correction capabilities.
Innovation Solution
An image processing apparatus that divides images into blocks, determines correct-solution frequency, weights block pairs based on this frequency, and performs majority voting to reduce errors and improve decoding performance by excluding 'dead region' blocks from the voting process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used to extract embedded codes, then the extraction process is simple, but decoding precision deteriorates due to dead regions causing errors that exceed error correction capabilities
Solution Approach 1:
The image is divided into multiple blocks, and each block is further divided into sub-blocks for individual processing. This segmentation allows the system to handle different regions with different characteristics, particularly separating dead regions from non-dead regions to apply appropriate processing strategies to each
Solution Approach 2:
Different processing methods are applied to different regions of the image based on their characteristics. Non-dead regions undergo error correction processing while dead regions are handled separately through weighting and majority voting, optimizing the overall decoding precision by adapting to local image properties
2Measurement precision
If majority voting is performed on all block pairs, then processing speed is maintained, but decoding precision deteriorates due to inclusion of dead region blocks with flat average pixel values
Solution Approach 1:
The set of all block pairs is segmented into two categories: dead region block pairs (with flat average pixel values) and non-dead region block pairs. This segmentation enables selective processing where only non-dead regions undergo error correction while dead regions use alternative weighting methods
Solution Approach 2:
The processing approach changes based on the parameter of average pixel value flatness. When the average pixel values of adjacent blocks are flat (dead region), a different processing path is taken involving weighting by correct-solution frequency rather than standard error correction, adapting the method to the local characteristics
Data Source
AI summary
A method of obtaining information included in an image, the method includes dividing the image into a plurality of pairs of blocks. The image includes a plurality of codes. Each of the codes includes a plurality of digits. The method further includes determining a digit value of each of the block pairs on the basis of difference of a degree of characteristic value between adjacent blocks in each of the pairs. The method further includes weighing each digit value of each of the block pairs. The method further includes deciding a new digit value of associated pairs of blocks on the basis of the majority of the weighed digit values of the associated pairs of the blocks and repeating the deciding in other associated pairs of the blocks to determine the code.