Compressed Video ML Pipeline for Artifact-Free Object Prediction
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Solution Overview
Problem
Machine learning models applied to compressed videos often generate inaccurate predictions due to artifacts introduced by video compression, leading to unrealistic presentations of XR elements and wasting resources.
Innovation Solution
The disclosed techniques process compressed videos using machine learning models trained to eliminate artifacts, ensuring accurate predictions without introducing additional artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are applied to compressed videos, then predictions can be generated, but artifacts from video compression cause inaccurate predictions and unrealistic XR presentations
Solution Approach 1:
The system performs decompression of the video stream before applying machine learning models. This preliminary action removes compression artifacts that would otherwise interfere with accurate object detection and tracking, enabling precise predictions while maintaining processing efficiency through optimized decompression algorithms
Solution Approach 2:
A dedicated decompression module serves as an intermediary between the compressed video source and the machine learning processing pipeline. This intermediary component handles the artifact removal function, allowing the ML models to receive clean video data without directly processing compressed artifacts, thus improving prediction accuracy
2Loss of energy
If video compression is applied to reduce bandwidth and storage, then resource efficiency improves, but artifacts are introduced that degrade ML model predictions
Solution Approach 1:
The system applies decompression as a preliminary step before ML processing, strategically removing artifacts only when necessary for accurate predictions. This approach maintains compression benefits for storage and transmission while eliminating artifacts only in the critical processing pipeline, balancing resource efficiency with prediction accuracy
Solution Approach 2:
The system applies different processing qualities to different parts of the video pipeline: compressed format is maintained for storage and transmission (resource efficiency), while decompressed format is used specifically for ML analysis regions where prediction accuracy is critical. This localized quality adjustment optimizes both bandwidth usage and prediction precision
3Productivity
If machine learning models process compressed videos with artifacts, then processing speed is maintained, but resource waste occurs due to inaccurate predictions
Solution Approach 1:
The system performs a quick decompression assessment to determine if artifact removal is necessary for the current video segment. This preliminary action enables the system to maintain fast processing speeds by skipping decompression when artifacts are minimal, while only applying the more resource-intensive decompression when needed for accurate predictions, thus reducing overall resource waste
Solution Approach 2:
The system applies partial decompression processing only to critical video regions containing objects of interest for ML analysis, rather than fully decompressing the entire video stream. This partial action approach maintains processing speed while reducing resource waste by focusing computational efforts only where prediction accuracy is most critical
Data Source
AI summary
Methods and systems are disclosed for applying machine learning models to compressed videos. The system receives a video, depicting an object, that has previously been compressed using one or more video compression processes. The system analyzes, using one or more machine learning models, the video that has previously been compressed to generate a prediction corresponding to the object depicted in the video, with one or more artifacts resulting from application of the one or more machine learning models to the video that has been previously compressed being absent from the prediction. The system generates a visual output based on the prediction in which the one or more artifacts are absent.