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Extensible man-machine cooperation image coding method and coding system

A technology of image coding and human-machine collaboration, applied in the field of image coding, can solve the problems of not guaranteeing image restoration and reconstruction, and quality cannot be guaranteed, and achieve the effects of guaranteed performance, excellent reconstruction quality, and guaranteed visual quality

Active Publication Date: 2021-07-16
PEKING UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, traditional lossy image compression schemes are only optimized for human vision and cannot guarantee the quality of machine vision
However, if we only consider compressing the features of machine vision tasks and do not guarantee the recovery and reconstruction of images, we cannot observe them under human vision.

Method used

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  • Extensible man-machine cooperation image coding method and coding system
  • Extensible man-machine cooperation image coding method and coding system
  • Extensible man-machine cooperation image coding method and coding system

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Embodiment Construction

[0059] In order to further illustrate the technical method of the present invention, the scalable man-machine cooperative image encoder of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific examples.

[0060] This example will focus on detailing the encoding process of the encoder and the training process of the decoder generation network in this technical method. Assume that we have built the required decoder generation network and have N training images {I 1 ,I 2 ,...,I N} as training data.

[0061] 1. Training process:

[0062] Step 1: Put {I 1 ,I 2 ,...,I N} in the edge map of each image after vectorization marked as {E 1 ,E 2 ,...,E N}, and the auxiliary information corresponding to key points is denoted as {C 1 ,C 2 ,...,C N}.

[0063] Step 2: According to the attached figure 1 As shown, the {E 1 ,E 2 ,...,E N} and {C 1 ,C 2 ,...,C N} into the generation network for forward pass. ...

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Abstract

The invention discloses an extensible man-machine cooperation image coding method and coding system. The method comprises the following steps: extracting an edge graph of each sample picture and vectorizing the edge graph as a compact representation for driving a machine vision task; performing key point extraction in the vectorized edge image to serve as auxiliary information; respectively carrying out entropy coding lossless compression on the compact representation and the auxiliary information to obtain two paths of code streams; carrying out preliminary decoding on the two paths of code streams to obtain an edge graph and auxiliary information; inputting the edge graph obtained by decoding and auxiliary information into a generative neural network, and performing forward calculation of the network; carrying out loss function calculation according to the obtained calculation result and the corresponding original picture, and carrying out back propagation on the calculated loss to a neural network to carry out network weight updating until the neural network converges, so as to obtain a double-path code stream decoder; obtaining an edge image and auxiliary information of a to-be-processed image, and encoding and compressing the edge image and the auxiliary information to obtain two paths of code streams; and enabling the two-path code stream decoder to decode the received code stream and reconstructs an image.

Description

technical field [0001] The invention belongs to the field of image coding, and relates to an expandable human-machine cooperative image coding method and a coding system. The invention can simultaneously improve the quality of images under human vision and machine vision. Background technique [0002] Lossy image compression is an indispensable key technology in the process of using and disseminating digital images. The traditional lossy image compression scheme transforms the image to obtain a compact representation, then continues quantization and entropy coding for compression, which greatly reduces the overhead of digital image storage and transmission, making digital images widely used in daily life. [0003] With the development of computer vision technology, more and more application scenarios need to consider the quality of images under machine vision, that is, images after lossy compression can still maintain performance equivalent to lossless images under machine v...

Claims

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Application Information

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IPC IPC(8): H04N21/2343H04N21/4402H04N19/132H04N19/13
CPCH04N21/2343H04N21/4402H04N19/13H04N19/132
Inventor 刘家瑛胡越予杨帅王德昭郭宗明
Owner PEKING UNIV
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