Video Frame ROI Detection via Chrominance Block Segmentation
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Solution Overview
Problem
Current methods for detecting regions of interest (ROI) in images are time-consuming and inefficient, making them unsuitable for real-time applications.
Innovation Solution
A method that divides a target video frame image into image blocks, acquires chrominance components, determines pixel ratios, and classifies region types based on these ratios to detect ROIs efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained using a large number of images with ROIs as training samples, then detection precision is improved, but detection efficiency deteriorates and processing time increases
Solution Approach 1:
The image is divided into multiple blocks, and each block is processed independently to determine region types. This segmentation approach reduces the computational complexity compared to processing the entire image through a trained neural network, thereby improving detection efficiency while maintaining acceptable precision through localized chrominance analysis
Solution Approach 2:
The method extracts chrominance components from image blocks and uses pixel ratio calculations to identify ROI regions. By extracting and analyzing only the essential chrominance features rather than using the full neural network processing pipeline, the method achieves faster detection speed while preserving key detection capabilities
2Measurement precision
If a neural network is trained using a large number of images with ROIs as training samples, then detection precision is improved, but processing time increases
Solution Approach 1:
Dividing the image into blocks enables parallel processing of multiple regions simultaneously, significantly reducing the total processing time compared to sequential neural network inference. Each block's chrominance analysis can be performed independently and concurrently, minimizing detection time while maintaining precision through systematic region classification
Solution Approach 2:
The method performs preliminary chrominance component extraction and pixel ratio calculation on image blocks before final ROI detection. This preliminary processing organizes and pre-computes essential features, reducing the time required for actual ROI identification while ensuring precision through structured analysis
3Productivity
If chrominance components are analyzed for each image block, then detection efficiency is improved, but device complexity increases
Solution Approach 1:
The detection apparatus processes image blocks independently using standardized chrominance analysis procedures. This modular segmentation allows each block to be handled by simple, repeatable operations rather than complex global processing, improving efficiency while keeping individual processing units relatively simple
Solution Approach 2:
The method changes the analysis parameters by focusing specifically on chrominance components and pixel ratios rather than using complex neural network parameters. This parameter simplification reduces computational complexity while maintaining detection efficiency through targeted chrominance-based region identification
Data Source
AI summary
A method and an apparatus for detecting a region of interest (ROI), an electronic device, and a readable storage medium are provided. In the method for detecting a ROI, a target video frame image is divided into a plurality of image blocks and chrominance components of pixels of each image block are acquired. A ROI of the target video frame image is detected by determining a region type of each image block according to a ratio of pixels in an image block meeting a preset chrominance condition to pixels of the image block.