Sensor-Guided Video Compression for Motion-Adaptive Data Rates
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Solution Overview
Problem
Existing video compression technologies, such as H.264/H.265, determine data rate based on image resolution and frames per second, leading to inefficient storage and transmission without considering dynamic changes in motion parameters, which can result in reduced video quality and image distortion.
Innovation Solution
A video compression method that utilizes sensors like radar, infrared, and laser to detect motion parameters, dynamically adjusting the inter-frame gap based on the detected motion to optimize compression parameters, minimizing quality loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video compression is performed using fixed parameters (image resolution and frames per second), then the data rate is determined, but storage efficiency is reduced and video quality deteriorates due to not considering dynamic motion changes
Solution Approach 1:
The patent applies dynamics by transitioning from fixed compression parameters to dynamic parameter adjustment based on real-time motion detection. The system continuously monitors motion parameters (such as object velocity and scene activity) and adapts the inter-frame gap and compression ratio accordingly, allowing the compression strategy to evolve with the content being encoded.
Solution Approach 2:
The patent implements parameter changes by modifying key compression parameters (inter-frame gap, compression ratio) based on detected motion characteristics. When motion is detected, the system adjusts these parameters to preserve quality in critical areas while maintaining compression efficiency in static regions, thereby resolving the contradiction between storage efficiency and video quality.
2Quantity of substance
If the inter-frame gap is increased to reduce data rate, then storage space is saved, but video quality is reduced due to insufficient motion capture
Solution Approach 1:
The system dynamically adjusts the inter-frame gap based on detected motion parameters. In scenes with high motion activity, the inter-frame gap is reduced to capture motion details, while in static scenes, the gap is increased to maximize compression. This dynamic adaptation resolves the contradiction between storage space and video quality.
Solution Approach 2:
The patent changes the inter-frame gap parameter according to motion detection results. When motion is detected beyond certain thresholds, the system reduces the inter-frame gap to maintain quality, whereas in static conditions, it increases the gap to save storage space, thereby optimizing the trade-off between these two parameters.
3Productivity
If compression ratio is increased to reduce transmission bandwidth, then bandwidth consumption is reduced, but image distortion increases
Solution Approach 1:
The patent applies local quality by differentiating compression strategies across different regions of the video frame based on motion detection. Areas with detected motion receive higher quality preservation (lower local compression ratio), while static areas undergo more aggressive compression. This localized approach reduces overall bandwidth consumption while maintaining image quality in motion-critical regions.
Solution Approach 2:
The system dynamically changes the compression ratio parameter based on motion parameters detected in different regions. When motion is detected, the compression ratio is reduced in those areas to prevent distortion, while static areas maintain higher compression ratios, optimizing the balance between bandwidth and image quality.
4Device complexity
If fixed frame rate is used to simplify encoding, then encoding complexity is reduced, but video quality deteriorates due to inability to adapt to motion changes
Solution Approach 1:
The patent introduces dynamics into the frame rate selection process by adjusting the frame rate based on detected motion parameters. In high-motion scenes, the frame rate is increased to capture motion details, while in static scenes, the frame rate is reduced to lower encoding complexity. This dynamic adaptation resolves the contradiction between complexity and video quality.
Data Source
AI summary
A video compression method and apparatus, and a computer-readable storage medium. The video compression method comprises: acquiring a detection result of a first sensor, and determining a target motion parameter according to the detection result; determining a video compression parameter according to the target motion parameter; and performing video compression on first video data according to the video compression parameter. A video compression ratio can be dynamically adjusted according to detection results of the first sensor, and thereby the efficiency of video compression, transmission, or storage is improved.