Video Enhancement Pipelining for Limited Processing Resources
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Solution Overview
Problem
Existing video processing methods are inefficient in enhancing video quality on electronic equipment with limited data processing capability, as they sequentially execute video enhancement tasks for each frame, leading to underutilization of processing resources and prolonged processing times.
Innovation Solution
A video processing method that simultaneously executes multiple video enhancement subtasks for different frames in a multi-thread concurrent manner, utilizing multiple threads to process video frames in a pipelining fashion, and allocates tasks between data processing chips to optimize resource utilization, including dividing AI video enhancement tasks into block tasks and utilizing idle compute sticks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video enhancement tasks are sequentially executed for each frame, then processing resource utilization is low, but processing time is prolonged
Solution Approach 1:
The patent implements continuous processing by maintaining a pipeline where multiple video frames are processed simultaneously at different stages. While one frame is being enhanced, the system concurrently prepares the next frame, ensuring that processing resources are continuously utilized without idle gaps between frame completions.
Solution Approach 2:
The video enhancement task is segmented into multiple independent subtasks that can be executed in parallel. Each subtask processes a specific aspect of video enhancement (e.g., denoising, sharpening, color correction) on different frames simultaneously, allowing the system to divide and conquer the overall processing workload.
2Productivity
If multiple video enhancement subtasks are executed concurrently, then data processing resource utilization improves, but system complexity increases
Solution Approach 1:
The patent introduces a task management intermediary layer that coordinates between the concurrent subtasks and the underlying processing resources. This intermediary handles task scheduling, resource allocation, and synchronization, thereby managing system complexity centrally rather than distributing it across all processing components.
Solution Approach 2:
The system dynamically adjusts the number and type of concurrent subtasks based on available processing resources and task priorities. The task execution model can adaptively scale the degree of parallelism, increasing concurrency when resources are abundant and reducing it when resources are constrained, thus managing complexity dynamically.
Data Source
AI summary
A video processing method and device, electronic equipment and a storage medium, which are applied to the technical field of computers. The method comprises: acquiring video data to be processed; generating video enhancement tasks corresponding to each video frame in the video data to be processed, the video enhancement task comprising a plurality of video enhancement subtasks; and simultaneously executing at least two different video enhancement subtasks of the plurality of video frames in a multi-thread concurrent manner, a single thread correspondingly executing one video enhancement subtask.