Video Change Detection via DCT Bitmaps and BLOB Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Real-time video transmission in applications like video conferencing faces challenges due to high data volumes and processor usage, particularly in face detection processes that introduce delays and consume excessive processing power.
Innovation Solution
A method that divides video images into N×N blocks, applies Discrete Cosine Transform (DCT) to calculate binary bitmaps representing change, uses OR operations and dilation/erosion processes to identify areas of change and movement, and detects Binary Large Objects (BLOBs) to frame areas of interest, significantly reducing processing costs compared to conventional face detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional face detection methods are used to identify areas of interest in real-time video communication, then face detection accuracy is maintained, but processor usage and time consumption increase significantly
Solution Approach 1:
The video image is divided into N×N blocks of pixel positions, and each block is processed independently through DCT transform to generate binary bitmaps. This segmentation allows parallel processing of multiple blocks simultaneously, reducing overall computation time while maintaining detection accuracy through comprehensive coverage of the entire video frame.
Solution Approach 2:
A binary bitmap is introduced as an intermediary representation between the original video data and the final face detection results. The bitmap encodes change information in a compressed binary format, serving as an efficient intermediate structure that reduces data volume for subsequent processing steps while preserving essential motion and change information needed for accurate face detection.
2Measurement precision
If conventional face detection methods are used to identify areas of interest in real-time video communication, then face detection accuracy is maintained, but time consumption increases significantly
Solution Approach 1:
The DCT transform and binary bitmap generation are performed as preliminary processing steps before the actual face detection algorithm is applied. By pre-processing the video data into a compact binary representation that highlights areas of change, the system prepares the data in advance, reducing the computational burden and time required for the subsequent face detection step.
Solution Approach 2:
The video data is transformed from the spatial domain to the frequency domain through DCT transform, changing the representation parameters from pixel values to frequency coefficients. This parameter transformation enables more efficient processing by concentrating energy in fewer coefficients and facilitating the generation of compact binary bitmaps that capture essential motion information with reduced data volume.
3Loss of energy
If video data is compressed using standard video coding methods, then data transmission efficiency is improved, but video quality and processing flexibility are compromised
Solution Approach 1:
The invention extracts only the essential change information from the video data through DCT transform and binary encoding, separating the critical motion and change components from the redundant static information. This extraction approach transmits only the necessary data for face detection and area of interest identification, improving transmission efficiency while maintaining the reliability needed for the specific application of face detection.
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 reduces processor and time consumption for face detection in real-time video communication without compromising accuracy, allowing for efficient processing of video frames and enabling more effective use of available resources.
Implementation Method 1
calculating a first bitmap representing a current time frame of the video image by a DCT (Discrete Cosine Transform) transform on each of the N×N blocks of pixel positions
Data Source
AI summary
The present invention relates to an image processing device and a method of framing changes and movements in a video image divided into N×N blocks of pixel positions. The method comprises calculating a first bitmap of the video image by a DCT transform on each of the N×N blocks of pixel positions, assigning a first binary value to the pixel positions of the N×N blocks when more than an amount of change, and a second binary value to the pixel positions of the N×N blocks when less than an amount of change. Calculating a third bitmap by an OR operation between a number of bitmaps representing past time frames of the video image, calculating a fourth bitmap by performing a dilation process of the third bitmap representing the current time frame of the video image, and creating one or more frames identifying area of changes and movements in the video image based on detecting BLOBs (Binary Large Objects) in the fourth bitmaps.