PBV Video Encoding by Image Region for Low-Latency Transmission
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Solution Overview
Problem
Existing purpose-built vehicles (PBVs) face challenges in efficiently transmitting video data to control devices while minimizing latency, especially in varying network conditions and resource usage scenarios.
Innovation Solution
The PBV employs a processor to divide driving images into sub-regions based on network status, location information, and resource usage, applying specific pre-processing options including resolution and bitrate, and encodes these sub-regions adaptively to optimize data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire driving image is encoded and transmitted at high resolution, then the image quality is improved, but the transmission latency and energy consumption increase
Solution Approach 1:
The patent divides the driving image into multiple regions of interest (ROIs) based on importance levels. Only critical regions are encoded at high resolution and transmitted with higher priority, while less important regions are encoded at lower resolution or transmitted with lower priority. This segmentation resolves the contradiction by maintaining high image quality for critical areas while reducing overall data transmission volume and latency.
Solution Approach 2:
The patent applies different encoding qualities to different regions of the image based on their importance. High-priority regions (e.g., obstacles, pedestrians) are encoded at high resolution with low latency requirements, while low-priority regions are encoded at lower resolution. This local quality differentiation maintains essential image quality while reducing overall transmission latency and energy consumption.
2Measurement precision
If the entire driving image is encoded and transmitted at high resolution, then the image quality is improved, but the energy consumption increases
Solution Approach 1:
The patent segments the image into multiple ROIs with different importance levels and encodes only the critical regions at high resolution. This reduces the total number of pixels that require high-computation encoding, thereby reducing energy consumption while maintaining sufficient image quality for safety-critical areas.
Solution Approach 2:
The patent applies high-quality encoding only to local regions that require it (high-priority ROIs), rather than uniformly encoding the entire image. This localized high-quality encoding maintains essential image quality for critical areas while significantly reducing the energy consumption associated with processing and transmitting the entire image at high resolution.
3Productivity
If region-based selective encoding is implemented, then the transmission efficiency is improved, but the device complexity increases
Solution Approach 1:
The patent divides the image into multiple ROIs based on importance levels and processes each region differently. This segmentation enables selective encoding and transmission optimization, improving transmission efficiency by focusing resources on critical regions while reducing overall data volume, despite adding some complexity to the encoding process.
4Adaptability or versatility
If adaptive encoding based on multiple parameters is implemented, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The patent implements dynamic adaptation of encoding parameters based on multiple factors including region importance, network conditions, and battery status. The system continuously adjusts encoding resolution, bitrate, and transmission priority according to current operating conditions, achieving high adaptability while managing complexity through parameter-based control strategies.
Data Source
AI summary
A purpose-built vehicle device includes a driving unit, a camera, a communication unit, and at least one processor connected to the driving unit, the sensor unit, and the communication unit. The at least one processor acquires a driving image of the PBV through the camera, divides the entire region of the driving image into one or more sub-regions based on at least one of the vehicle's network status, the vehicle's location information, or the vehicle's resource usage rate, determines a pre-processing option for the one or more sub-regions, and encodes the one or more sub-regions of the driving image based on the pre-processing option to obtain one or more pieces of encoded data. The pre-processing option can include resolution and bitrate.