Image Metadata Generation via Frequency Band Segmentation
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Solution Overview
Problem
Display devices face challenges in efficiently processing images with many edges, as they require significant hardware resources to accurately analyze frequency characteristics, which affects image sharpness and realism.
Innovation Solution
A method and device for generating metadata that includes frequency characteristic information of an image by using discrete cosine transform (DCT) coefficients, dividing blocks into frequency bands, and determining band values based on threshold comparisons, allowing for clustering into high-frequency, mid-frequency, or low-frequency images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If display devices process images with many edges to accurately analyze frequency characteristics, then image sharpness and realism are improved, but hardware resource consumption increases significantly
Solution Approach 1:
The image is divided into multiple blocks, and each block is further divided into frequency bands (low-frequency, mid-frequency, high-frequency regions). This segmentation allows the device to analyze frequency characteristics of individual blocks rather than processing the entire image uniformly, reducing overall hardware resource consumption while maintaining analysis accuracy for edge-rich regions.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their frequency characteristics. Blocks with high-frequency characteristics (containing edges) receive more detailed analysis, while low-frequency blocks use simpler processing. This local quality approach ensures accurate frequency analysis where needed while conserving hardware resources in regions requiring less processing.
2Measurement precision
If display devices use more hardware resources to process images with many edges, then image sharpness and realism are improved, but processing efficiency decreases
Solution Approach 1:
The patent applies full frequency analysis only to blocks that require it (those with high-frequency characteristics), while using simplified processing for other blocks. This partial action approach maintains measurement precision for critical regions while improving overall processing efficiency by avoiding unnecessary detailed analysis in regions where it is not needed.
Solution Approach 2:
By segmenting the image into blocks and analyzing their frequency characteristics separately, the device can identify and focus computational resources only on blocks containing edges or high-frequency content. This segmentation strategy improves processing efficiency by avoiding uniform high-cost processing across the entire image while maintaining accuracy where required.
3Measurement precision
If the image is divided into multiple frequency bands for detailed analysis, then frequency characteristic accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides each image block into three frequency bands (low-frequency, mid-frequency, high-frequency regions) and analyzes them separately. This segmentation provides detailed frequency characteristic information while keeping the processing structure manageable by using a consistent three-band division across all blocks, avoiding the need for complex variable-band processing.
Solution Approach 2:
The patent determines frequency characteristics by comparing statistics of frequency coefficients against threshold values for each band. This parameter-based approach (using threshold comparisons) provides accurate frequency classification while maintaining relatively simple device structure, avoiding the need for complex machine learning models or sophisticated signal processing algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more efficient use of hardware resources and improved image realism by accurately analyzing and transmitting frequency characteristics, allowing for better image processing and display, particularly in UHD displays.
Implementation Method 1
The frequency coefficients may include discrete cosine transform (DCT) coefficients generated by performing DCT on the pixel values
Data Source
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AI summary
Provided are a method and device for generating metadata including frequency characteristic information of an image. Pixel values of a current block among blocks divided from the image are converted into frequency coefficients in the frequency domain. A band value of a frequency band corresponding to each of regions of the current block is determined using the frequency coefficients included in the regions of the current block, the regions of the current block being divided to correspond to different frequency bands. Metadata including the frequency characteristic information of the current block is generated based on the determined band values.