Parallel Video Decoding Architecture for Low-Latency Indexing
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Solution Overview
Problem
Current video analysis systems face computational inefficiencies in serially decoded indexing processes, leading to performance bottlenecks, particularly in deep learning inference processes.
Innovation Solution
Implementing a parallel decoding and indexing architecture that includes a parallel decoder, a parallel process planner, and separate inference servers to manage computation-heavy tasks, allowing for efficient and simultaneous processing of video segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If serial decoding and indexing processes are used, then system complexity is reduced, but processing speed and productivity deteriorate
Solution Approach 1:
The patent divides the video processing system into multiple independent decoder instances, each capable of processing different video segments simultaneously. This segmentation allows parallel processing of video streams, significantly improving productivity while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent transitions from sequential (one-dimensional) processing to parallel (multi-dimensional) processing by deploying multiple decoder instances across different processing units. This dimensional change enables simultaneous processing of multiple video segments, resolving the contradiction between speed and complexity.
2Productivity
If parallel decoding is implemented, then processing efficiency improves, but system complexity and resource requirements worsen
Solution Approach 1:
The patent creates universal decoder instances that can handle multiple video formats and segments through standardized interfaces. This multi-functionality allows parallel processing without proportionally increasing complexity, as each decoder instance serves multiple purposes across different video streams.
Solution Approach 2:
The patent utilizes configurable parameters in the decoder architecture that allow dynamic adjustment of processing modes, thread counts, and resource allocation. These parameter changes enable efficient parallel processing while maintaining system adaptability and preventing complexity from scaling linearly with processing power.
3Measurement precision
If deep learning inference processes are applied to video analysis, then analysis accuracy improves, but computational time and processing latency worsen
Solution Approach 1:
The patent performs preliminary decoding and segment extraction before applying deep learning inference. By preparing video segments in advance through parallel decoding, the system reduces the computational burden during the inference stage, thereby maintaining high accuracy while reducing overall processing latency.
Solution Approach 2:
The patent implements continuous parallel decoding that feeds segments to inference processes without idle waiting time. This continuity ensures that deep learning inference always has processed segments available, maximizing utilization of computational resources and reducing total processing time while maintaining analysis accuracy.
Data Source
AI summary
A video analysis system performs indexing of one or more videos in parallel by using pipelines executed by task processors. A task processor may include compute resources configured on a cloud infrastructure or on-premise compute resources. The video analysis system receives requests to index one or more videos. In one instance, the requests may be from users of client devices with requests to index the videos, such that the indexed information can be used to perform downstream applications, such as search query-based retrieval, and the like. The video analysis system performs the indexing process in parallel, so that significant bottlenecks can be eliminated compared to existing methods.