Contextual Video Learning for Bandwidth Reduction
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Solution Overview
Problem
Current video encoding techniques do not effectively reduce video data rates, leading to high bandwidth and computational resource demands, and fail to incorporate contextual learning to optimize video transmission.
Innovation Solution
A video encoding/decoding system that segments raw image frames to identify objects of interest, ranks them, generates frame keys, and uses contextual video learning to determine a training metric for efficient data transmission, allowing for the transport of frame keys or encoded frames based on loss metric tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If typical video encoding techniques are used, then video data rate is reduced to some extent, but bandwidth and computational resource demands remain high
Solution Approach 1:
The video sequence is segmented into multiple objects of interest with different importance ranks. The encoder processes and transmits only the most important objects (e.g., rank 1 objects) at full resolution, while less important objects are either transmitted at lower resolution or reconstructed from stored model data. This segmentation by importance level enables selective transmission that significantly reduces bandwidth consumption while maintaining perceived video quality.
Solution Approach 2:
The system dynamically changes transmission parameters based on object importance rank. For high-rank objects, full-resolution data is transmitted; for medium-rank objects, reduced-resolution data is transmitted; for low-rank objects, only motion vectors or no data is transmitted, relying on stored model data for reconstruction. This parameter adaptation based on contextual learning achieves optimal bandwidth utilization.
2Reliability
If more video data is transmitted to maintain quality, then bandwidth consumption increases, but transmission efficiency decreases
Solution Approach 1:
The encoder performs preliminary analysis of each video frame to identify and rank objects of interest before transmission. By pre-determining which objects require full transmission and which can be reconstructed from stored data, the system optimizes bandwidth allocation in advance, ensuring critical quality elements are transmitted while minimizing unnecessary data transmission.
Solution Approach 2:
The system creates and maintains stored model data (copies) of video scenes at the decoder side. Instead of transmitting complete video data, the encoder transmits only the differences or updates needed to refresh the stored models. This copying approach maintains video quality for important objects while dramatically reducing bandwidth consumption.
3Productivity
If traditional video compression is applied, then data rate is reduced, but contextual information is lost reducing transmission optimality
Solution Approach 1:
The system implements a feedback mechanism where the encoder analyzes video content, identifies objects of interest, and determines transmission priorities based on contextual understanding. The decoder uses received data to reconstruct video and update stored models, feeding this information back to improve future encoding decisions. This feedback loop preserves contextual information while optimizing data rate reduction.
Solution Approach 2:
The encoder dynamically adjusts compression parameters based on contextual video information. For important objects identified through contextual analysis, lower compression ratios are applied to preserve detail. For less important objects or background elements, higher compression ratios are used. This adaptive parameter change maintains essential contextual information while achieving aggressive overall data rate reduction.
Data Source
AI summary
Aspects of the disclosure are directed to a video system. In one aspect, a video encoding/decoding method or apparatus includes segmenting each frame of a sequence of raw image frames to derive objects of interest; ranking the objects of interest to generate a ranks and ranked objects of interest; generating frame keys corresponding to the objects of interest; retrieving a source stored model data; and generating a training metric using the frame keys and the source stored model data. In another aspect, it includes receiving frame keys from a source portion; retrieving a destination stored model data and sending the destination stored model data to a destination contextual video learning system; and retrieving objects of interest from the destination stored model data using the frame keys and the destination contextual video learning system.