Dynamic Feature Extraction for Video Bandwidth Constraints
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Solution Overview
Problem
Existing image processing techniques for matching and retrieval, such as those described in ISO/IEC JTC1/SC29/WG11/W12929, do not account for bandwidth or bitrate constraints, leading to inefficiencies in data transmission and computational resource usage during image feature extraction and comparison.
Innovation Solution
A system and method that dynamically adjust the number of features extracted from digital video frames based on a target bitrate value, optimizing resource usage by encoding and transmitting compact descriptors efficiently, thereby improving precision and channel occupancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If compact descriptors are extracted and transmitted for image matching and retrieval, then matching precision is improved, but bandwidth consumption increases
Solution Approach 1:
The system dynamically adapts the number of features extracted from each frame based on available bandwidth conditions. When bandwidth is abundant, more features are extracted to improve precision; when bandwidth is constrained, fewer features are extracted to reduce transmission load. This dynamic adjustment resolves the contradiction by making the system flexible rather than static.
Solution Approach 2:
The patent changes the parameter of feature quantity (number of features extracted) based on bandwidth availability. By adjusting this parameter dynamically, the system optimizes the trade-off between matching precision (which improves with more features) and bandwidth consumption (which increases with more features).
2Measurement precision
If more features are extracted from each video frame, then retrieval accuracy is improved, but computational resource usage increases
Solution Approach 1:
The system dynamically adjusts the number of features extracted based on computational resource availability and bandwidth conditions. Rather than always extracting the maximum number of features, the system adapts the feature quantity to match available resources, thereby improving retrieval accuracy when resources permit while conserving computational power when resources are constrained.
3Loss of information
If feature extraction is performed on every frame, then completeness of data is improved, but transmission efficiency deteriorates under bandwidth constraints
Solution Approach 1:
Instead of extracting features from every frame (excessive action), the system selectively extracts features from only those frames that meet certain criteria or represent significant changes. This partial action approach maintains data completeness for important frames while improving transmission efficiency by skipping redundant frames.
Solution Approach 2:
The system extracts only the necessary features from video frames rather than processing all frames uniformly. By selectively extracting features based on bandwidth availability and frame importance, the system maintains essential data completeness while improving overall transmission efficiency.
Data Source
AI summary
In an embodiment, digital video frames in a flow are subjected to a method of extraction of features including the operations of: extracting from the video frames respective sequences of pairs of keypoints/descriptors limiting to a threshold value the number of pairs extracted for each frame; sending the sequences extracted from an extractor module to a server for processing with a bitrate value variable in time; receiving the aforesaid bitrate value variable in time at the extractor as target bitrate for extraction; and limiting the number of pairs extracted by the extractor to a threshold value variable in time as a function of the target bitrate.