Low-code-rate video coding and decoding method based on image reconstruction convolutional neural network

A convolutional neural network and video encoding and decoding technology, which is applied in the field of low-bit-rate video encoding and decoding, can solve problems such as video compression and distortion, achieve the effect of enhancing learning ability, overcoming weak learning ability, and improving the quality of upsampled images

Active Publication Date: 2019-08-02
XIDIAN UNIV
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Problems solved by technology

It solves the problem of serious compression distortion of video after low-b...

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  • Low-code-rate video coding and decoding method based on image reconstruction convolutional neural network
  • Low-code-rate video coding and decoding method based on image reconstruction convolutional neural network
  • Low-code-rate video coding and decoding method based on image reconstruction convolutional neural network

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Embodiment Construction

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] refer to figure 1 , to further describe in detail the specific steps for realizing the present invention.

[0051] Step 1, input video.

[0052] Input a video consisting of multiple images of the same resolution.

[0053]Step 2, extract an unselected image from the input video.

[0054] Step 3, downsampling the selected image.

[0055] Using the bicubic interpolation method, the extracted image is down-sampled by 2 times to obtain a down-sampled low-resolution image.

[0056] The steps of the described double cubic interpolation Bicubic method are as follows:

[0057] The first step is to calculate the weight of each pixel in the surrounding 4x4 pixel block centered on each pixel in each downsampled image in the low-resolution video according to the following formula:

[0058]

[0059] in, Represents the weight of the rth pixel in the xth row, the yth...

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Abstract

The invention provides a low-code-rate video coding and decoding method based on an image reconstruction convolutional neural network. The method is used for solving the problem that in the prior art,a video is seriously compressed and distorted after being coded and decoded at a low code rate. The implementation steps are as follows: carrying out down-sampling operation on the input video to obtain a low-resolution video; and performing video encoding and decoding on the low-resolution video by using a standard X265 codec to obtain a decoded low-resolution video, inputting the decoded low-resolution video into the trained image reconstruction convolutional neural network, and then obtaining a reconstructed video with the same resolution as the input video. According to the invention, serious compression distortion caused by video coding and decoding can be effectively inhibited at a low code rate, and the video quality can be well improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a low-bit-rate video encoding and decoding method based on image reconstruction convolutional neural network in the technical field of video image processing. The invention can be used for compression coding and decoding of low bit rate video formed by natural images. Background technique [0002] Coding of video images is an effective means to reduce video redundant data during video transmission. There are many standard algorithms for video image coding and decoding. The latest generation of high-efficiency video coding standards as the latest video compression standards have achieved good results. The AVC standard saves about 50% of the code stream, but it also greatly increases the encoding complexity. This standard framework continues to use the previous international standard hybrid coding framework. The main implementation steps of the coding framework are...

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Application Information

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IPC IPC(8): H04N19/85H04N19/593H04N19/176H04N19/80G06N3/04G06N3/08
CPCH04N19/85H04N19/593H04N19/176H04N19/80G06N3/08G06N3/045
Inventor 何刚王正蒋昊李云松
Owner XIDIAN UNIV
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