Wireless Video Compression Control for Low-Latency Driving Streams
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Solution Overview
Problem
In autonomous driving scenarios, wireless video transmission faces challenges with transmission channel fluctuation and instability, leading to packet loss and latency issues that affect video quality, causing freezing and mosaic on the display.
Innovation Solution
A method and apparatus that utilize a neural network model to predict optimal compression parameters for key image blocks based on previous video frames, adapting compression rates to ensure video quality and meet low-latency requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Weight of moving object
If wireless short-range communication is used to transmit video streams, then cable quantity and vehicle weight are reduced, but transmission channel stability deteriorates causing packet loss and latency fluctuation
Solution Approach 1:
The video stream is segmented into multiple video frames that are transmitted separately over the wireless channel. Each frame can be independently received and decoded, allowing the system to handle packet loss at the frame level rather than causing complete transmission failure. This segmentation enables graceful degradation where received frames can still be displayed even if some are lost during wireless transmission.
Solution Approach 2:
The system performs preliminary decoding of received video frames before display, allowing early detection of missing or corrupted frames. This preliminary processing enables the system to prepare compensation strategies in advance, such as using motion estimation to predict missing frame content or adjusting display timing to maintain continuous video output despite transmission uncertainties.
2Quantity of substance
If compression coding is applied to reduce data transmission volume, then bandwidth consumption is reduced, but video quality deteriorates causing freezing and mosaic
Solution Approach 1:
The patent applies different compression strategies to different regions of the video frame based on their importance. Critical regions such as those containing key objects or high-motion areas receive lower compression (higher quality), while less important background regions receive higher compression. This regional differentiation maintains overall video quality while reducing total data volume, preventing freezing and mosaic effects in critical areas.
Solution Approach 2:
The system transmits additional redundant information beyond what strict compression would require, such as extra motion vectors or reference frame data for critical regions. This partial excessive action ensures that even under high compression ratios, the decoder can reconstruct important video content accurately, preventing quality degradation that would cause freezing or mosaic artifacts.
3Loss of time
If high compression parameters are used to reduce latency, then transmission speed increases, but video quality deteriorates causing display artifacts
Solution Approach 1:
The compression parameters are dynamically adjusted based on real-time channel conditions and content characteristics. When the wireless channel is good or the video content is simple, higher compression is applied to reduce latency. When the channel degrades or complex content is detected, compression is relaxed to maintain quality. This dynamic adaptation allows the system to optimize the latency-quality tradeoff continuously, preventing artifacts while maintaining low latency when possible.
Data Source
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AI summary
Provided are a video transmission method and apparatus, a storage medium, and a system. The method includes: performing decoding processing on M first video frames from a second apparatus to obtain M second video images, where the first video frame is obtained through compression coding processing on a first video image based on a first compression parameter, the first video image belongs to a first video stream, and M is an integer greater than or equal to 1; predicting, based on content of the M second video images, a second compression parameter corresponding to a key image block that is included in a third video image, where capture time of the third video image is later than capture time of the first video image; and sending the second compression parameter to the second apparatus, where the second compression parameter is used to perform compression coding processing on an image block that is included in the third video image. In a video transmission scenario with a high requirement for real-time transmission, this method helps ensure video quality to a maximum extent while satisfying a network transmission requirement and a low-latency condition.