Dynamic Video Encoding Prediction Structure for AI Frame Prioritization
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Solution Overview
Problem
In distributed artificial intelligence applications, video decoding for AI processing is computationally and memory intensive, as existing video encoders use flat encoding prediction structures that require decoding of all frames in a group of pictures, even if not all frames are useful for AI processing, leading to resource constraints.
Innovation Solution
Implementing enhanced video codecs with a dynamic encoding prediction structure that skips decoding of frames unlikely to be useful for AI processing, using a suitable-for-inference-factor (SIF) score to determine frame usefulness, and assigning frames to layers based on their SIF scores, allowing only useful frames to be decoded and processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If flat encoding prediction structure is used, then video encoding compatibility is maintained, but computational resources are wasted decoding unnecessary frames
Solution Approach 1:
The video stream is segmented into different layers based on SIF scores, with high-priority frames (SIF ≥ threshold) placed in Layer 0 and low-priority frames in Layer 1. This segmentation allows the decoder to selectively process only Layer 0 frames for AI tasks, avoiding computation on unnecessary frames while maintaining encoding compatibility through standard NAL unit structures.
Solution Approach 2:
The encoder performs preliminary classification of frames by calculating SIF scores and assigning frames to appropriate layers before transmission. This preliminary action embeds prioritization information in the bitstream, enabling the decoder to skip low-priority frames without performing AI processing, thus conserving computational resources before the actual AI inference stage.
2Reliability
If all frames are decoded for AI processing, then processing completeness is ensured, but memory resources are excessively consumed
Solution Approach 1:
The patent extracts and processes only the essential subset of frames (Layer 0 frames with SIF ≥ threshold) that are most relevant for AI processing. By taking out only these high-priority frames from the complete video stream, the system ensures adequate AI processing coverage while reducing memory consumption by excluding low-priority frames (Layer 1) that contribute minimally to AI task outcomes.
3Loss of energy
If dynamic encoding prediction structure is implemented, then resource efficiency is improved, but encoder complexity increases
Solution Approach 1:
The encoder implements dynamic frame prioritization by calculating SIF scores for each frame based on AI task requirements and motion characteristics. This dynamic approach adjusts the priority assignment in real-time, allowing the system to adapt to varying video content and AI task demands. The dynamics are managed through programmable logic that evaluates frame importance metrics and assigns appropriate layers, balancing complexity with resource efficiency.
Data Source
AI summary
Example systems, apparatus, articles of manufacture, and methods that implement video encoders and decoders for artificial intelligence applications are disclosed. Example apparatus disclosed herein are to assign a video frame to one of a plurality of layers of an encoding prediction structure based on at least one of a video metric or an artificial intelligence metric associated with the video frame. Disclosed example apparatus are also to provide prediction layer metadata for the video frame to a video encoder that is to encode the video frame in a video stream, the prediction layer metadata to identify the one of the plurality of layers.