Multi-Source UHD Video Transport via Parallel HD Channels
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Solution Overview
Problem
Conventional video architectures fail to support the bandwidth and timing requirements of ultra-high definition (UHD) sensors, leading to inefficiencies and high costs in adapting existing HD video infrastructure for UHD data transport, and compression techniques lose essential metadata needed for non-human viewable image processing applications.
Innovation Solution
A system and method that divides UHD video data into lossless segments, using existing HD video architectures by parallelizing data across multiple channels with metadata for reconstruction, employing pixel packing and dynamic bandwidth management to transport UHD data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression techniques are used to transport UHD video data over existing HD video architectures, then bandwidth requirements are reduced and existing infrastructure can be utilized, but loss of raw sensor data and metadata occurs that is needed for non-human viewable image processing applications
Solution Approach 1:
The UHD video data is divided into multiple separate HD video streams, each carrying a portion of the original data. This segmentation allows the data to be transmitted over existing HD infrastructure without compression, preserving all raw sensor information while distributing the bandwidth requirement across multiple channels.
Solution Approach 2:
Multiple HD video streams are nested within the UHD data structure, with each stream containing a specific portion of the original UHD data. The nested structure includes metadata that enables reconstruction of the complete UHD video from the individual HD streams, allowing lossless recovery of raw sensor data.
2Speed
If new video architectures are developed from scratch to support UHD sensors, then bandwidth and timing requirements are met, but existing video hardware infrastructure cannot be utilized and costs increase
Solution Approach 1:
The UHD data stream is segmented into multiple HD-compatible streams that can be processed by existing video hardware. This segmentation allows the system to leverage established HD video infrastructure while maintaining the high data transmission rates required for UHD sensors through parallel processing of multiple streams.
Solution Approach 2:
The system makes existing HD video hardware multi-functional by enabling it to handle UHD data through parallel stream processing. The same existing video architecture that processes standard HD video can now process segmented UHD data, eliminating the need for completely new specialized hardware while maintaining UHD performance requirements.
3Adaptability or versatility
If legacy HD video hardware is used to transport UHD video data, then existing infrastructure can be utilized, but sufficient bandwidth is not provided for raw UHD data transport
Solution Approach 1:
The UHD data is divided into multiple HD streams that can be transmitted simultaneously over legacy hardware. This segmentation multiplies the effective bandwidth by distributing the data load across multiple parallel channels, allowing existing HD video hardware to handle UHD data volumes that exceed the capacity of any single channel.
Solution Approach 2:
The system transitions from single-channel UHD transmission to multi-channel HD transmission, adding the dimension of parallel processing. By distributing UHD data across multiple HD streams transmitted simultaneously, the system overcomes the bandwidth limitations of individual legacy HD channels while maintaining compatibility with existing hardware infrastructure.
Data Source
AI summary
A sensor data processing apparatus can be coupled to multiple image sensors of different types. The apparatus adjusts frame transmission rates based on the number of sensors and type of image data sourced by the sensors to optimize utilization of bandwidth on a number of transport channels. The apparatus can be configured to transport selected frames in the image data that are identified as critical frames at a higher rate than non-selected frames in the image data.