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US 2022/0021887 A1Published application
Apparatus for Bandwidth Efficient Video Communication Using Machine Learning Identified Objects Of Interest
AssigneeWisconsin Alumni Research Foundation
InventorsSuman Banerjee, Peng Liu, Varun Chandrasekaran
Life of this patent
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Abstract
A video compression/decompression system employs a machine learning model to extract regions of interest from the input video to define relatively higher bit rate portions of the video frames that are transmitted. The resulting compressed data may be transmitted using standard protocols without specialized decoders but may optionally include a second machine learning model trained at the transmitter to boost the resolution of the reconstructed compressed data emphasizing the region of interest.
Claims16
A video compression system comprising:
- a region of interest extractor receiving an input stream of a first set of video frames and identifying a region of interest by applying the input stream of the first set of video frames to a machine learning model trained to identify a predetermined physical object in the input stream of the first set of video frames by defining a region of interest extracting the predetermined physical object, the training of the machine learning model employing a training set linking a second set of video frames depicting the predetermined physical object to the predetermined physical object;
- a bit rate compressor receiving an input stream of the first set of video frames and the region of interest from the region of interest extractor and outputting an output stream of video frames based on both the input stream of the first set of video frames and a region of interest defining a first portion of the first set of video frames of the input stream;
- wherein the bit rate compressor encodes the first portion of the first set of video frames at a relatively higher bit rate than a second portion of the first set of video frames outside of the first portion.
Dependencies are inferred from the claim text.