- Patent Title: Image compression with bounded deep neural network perception loss
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Application No.: US16526335Application Date: 2019-07-30
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Publication No.: US11010929B2Publication Date: 2021-05-18
- Inventor: Xiufeng Xie , Kyu-Han Kim
- Applicant: Hewlett Packard Enterprise Development LP
- Applicant Address: US TX Houston
- Assignee: Hewlett Packard Enterprise Development LP
- Current Assignee: Hewlett Packard Enterprise Development LP
- Current Assignee Address: US TX Houston
- Agency: Hewlett Packard Enterprise Patent Department
- Main IPC: G06T9/00
- IPC: G06T9/00 ; G06F17/14 ; G06N5/04 ; G06N3/04

Abstract:
Example method includes: transmit a plurality of probe images from an Internet of Things (IoT) device at an edge network to a server hosting a target deep neural network (DNN), wherein the plurality of images are injected with a limited amount of noise; receive a feedback comprising a plurality of discrete cosine transform (DCT) coefficients from the server hosting the target DNN, wherein the plurality of DCT coefficients are unique to the target DNN; generate a quantization table based on the feedback received from the server hosting the target DNN; compress a set of real-time images using the generated quantization table by the IoT device at the edge network; and transmit the compressed set of real-time images to the server hosting the target DNN for DNN inferences.
Public/Granted literature
- US20210035330A1 IMAGE COMPRESSION WITH BOUNDED DEEP NEURAL NETWORK PERCEPTION LOSS Public/Granted day:2021-02-04
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