Double-code-stream face resolution fidelity video encoding and decoding method for Internet of Things monitoring
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A technology for monitoring video, code stream and human face, which is applied in the field of monitoring video coding, and can solve the problems of incapable of face resolution, rate fidelity, etc.
Active Publication Date: 2019-10-11
SUZHOU INSTITUE OF WUHAN UNIV +1
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[0011] In view of this, the present invention provides a dual-stream human face resolution-fidelity video encoding and decoding method for Internet of Things monitoring, to solve or
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Embodiment 1
[0077] This embodiment provides a video encoding and decoding method with double-stream human face resolution and fidelity for Internet of Things monitoring, please refer to figure 1 , the method includes:
[0078] Step S1: Obtain the original surveillance video image, use the MTCNN convolutional neural network to detect the key frames in the surveillance video image, use the KCF algorithm to track multiple faces in the non-key frames in the surveillance video image, and obtain the original surveillance video image Native resolution images of faces for all frames.
[0079] Specifically, step S1 is to extract face elements in the original surveillance video image, and different methods are used for key frames and non-key frames in the surveillance video image.
[0080] In one embodiment, step S1 specifically includes:
[0081] Step S1.1: Obtain the original surveillance video image through the high-resolution surveillance camera, and perform image preprocessing on the origina...
Embodiment 2
[0137] This embodiment provides a video decoding method for double-stream human face resolution fidelity for Internet of Things monitoring, the method comprising:
[0138] Using the dual-stream face recovery decoding algorithm, the face recovery layer code stream is decoded to obtain a monitoring picture with local resolution and fidelity of the face.
[0139] Among them, the double-code stream face recovery decoding algorithm is used to decode the code stream of the face recovery layer, including:
[0140] Synchronously receive the code stream packets corresponding to the basic code stream and the face restoration layer code stream, analyze the code stream packets, and obtain the difference information code stream between the basic code stream and the face recovery layer;
[0141] Decoding the basic code stream, and upsampling the decoded video image by bilinear interpolation, so that the video is enlarged to the original resolution size, and the first original resolution ima...
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Abstract
The invention discloses a double-code-stream face resolution fidelity video coding and decoding method for Internet of Things monitoring, and the method comprises the steps: firstly extracting face elements in a monitoring video image, and employing an MTCNN convolution neural network to detect and track a face; then, down-sampling and encoding the original monitoring video to obtain a low-resolution base layer code stream; and then, filling an area corresponding to the upsampled image with the original resolution image of the face, subtracting the upsampled restored image of the low-resolution image to obtain difference information of a face area, and encoding the difference information to obtain a face restoration layer code stream. The invention further provides a corresponding decodingmethod. A double-code-stream face recovery decoding algorithm is adopted, and a monitoring picture with fidelity of the local resolution of the face is obtained through decoding. Local resolution fidelity images of face high definition and background low definition are fused. Rich detail information of key areas in scene elements is reserved. Video coding rate is greatly reduced, the compressionrate of monitoring videos is increased, and the method has very high practicability.
Description
technical field [0001] The invention relates to the technical field of monitoring video encoding, in particular to a video encoding and decoding method for double-code stream face resolution preservation for Internet of Things monitoring. Background technique [0002] Urban video surveillance systems are playing an increasingly important role in the field of public safety. With the increase of deployment and control range and the improvement of picture definition requirements, the amount of surveillance video data generated every day is also increasing, which makes the existing energy consumption, transmission and storage costs increase day by day. How to maintain the analyzability of surveillance video while reducing the cost of surveillance camera access and network transmission has become an important issue that must be solved. [0003] Under the existing technical conditions, in response to the demand for wide coverage and massive access of urban surveillance, the Inter...
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