Machine Vision Bit Rate Control for Encoder Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video compression systems often inefficiently manage bit rate, either undershooting or overshooting the target when using distortion metrics designed for human vision, leading to suboptimal performance for machine vision tasks, and face challenges in scalable and efficient bit rate control for video and point cloud data across multiple sensors.
Innovation Solution
A control system with a machine vision algorithm that dynamically adjusts the bit rate of encoders based on detected content, using predefined classes and confidence scores to set target bit rates, allowing for efficient bit allocation and reducing unnecessary bit usage by prioritizing high quality for objects of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If distortion metrics designed for human vision (MSE, PSNR, MS-SSIM) are used to control bit rate, then quality for human observers is improved, but bit rate efficiency for machine vision tasks deteriorates
Solution Approach 1:
The patent changes the evaluation metric from human-vision-based distortion measures (MSE, PSNR, MS-SSIM) to machine-vision-performance-based metrics. The control system dynamically adjusts bit rate by evaluating how well machine vision tasks perform on encoded content, rather than measuring traditional distortion. This parameter change aligns the rate control objective with the actual usage scenario, improving bit rate efficiency for machine vision applications.
2Manufacturing precision
If bit rate is increased to ensure sufficient quality for all content, then quality for hard content is improved, but bandwidth utilization deteriorates due to overspending on easy content
Solution Approach 1:
The patent implements dynamic bit rate adjustment based on content characteristics and machine vision task requirements. The control system continuously monitors encoded content and adjusts bit rate in real-time, increasing it only when machine vision performance degrades and decreasing it when performance is sufficient. This dynamic approach replaces static or capacity-based rate control, optimizing bandwidth utilization while ensuring adequate quality for machine vision tasks.
Solution Approach 2:
The system employs feedback mechanisms where the control system evaluates machine vision task performance on encoded content and uses this information to adjust subsequent encoding parameters. The feedback loop measures actual machine vision performance (not theoretical distortion) and closes the control cycle by adjusting bit rate accordingly, ensuring efficient bandwidth usage matched to actual task requirements.
3Productivity
If bit rate is decreased to save bandwidth on easy content, then bandwidth utilization is improved, but quality for machine vision tasks deteriorates due to insufficient bits
Solution Approach 1:
The system dynamically adjusts bit rate based on actual machine vision task performance rather than using fixed or pre-determined rates. When content requires higher quality for successful machine vision processing, the control system increases bit rate. When content can be adequately processed at lower rates, bandwidth is reduced. This dynamic adaptation ensures both efficient bandwidth utilization and sufficient quality for machine vision tasks.
4Device complexity
If fixed quantization setting is used, then encoder complexity is reduced, but bit rate control deteriorates leading to capacity overshooting on hard content
Solution Approach 1:
The patent introduces a control system with feedback loops that monitor encoded content quality and machine vision task performance, then adjust encoding parameters including quantization settings. This feedback-based control replaces fixed quantization, enabling the encoder to adapt to content complexity and task requirements, preventing capacity overshooting on hard content while maintaining reasonable encoder complexity through automated control.
Data Source
AI summary
A method by a control system includes receiving at least one of an image, a video frame, or a point cloud frame. Responsive to detecting an object of a group of classes of a predefined group of classes in the at least one of the image, the video frame, or the point cloud frame, setting a target bit rate of an encoder to a specific value based on the object. Responsive to not detecting any object that belongs to any of the predefined group of classes in the at least one of the image, the video frame, or the point cloud frame, setting the target bit rate to one of a default bit rate and a current bit rate. The method includes sending an instruction to the encoder of a bit rate to use based on whether an object is detected.