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JPEG image decompression effect method based on DCT coefficient prediction

A decompression and coefficient technology, applied in image communication, digital video signal modification, biological neural network model, etc., can solve problems such as easy ambiguity and high complexity

Inactive Publication Date: 2021-01-12
SICHUAN UNIV
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Problems solved by technology

But reconstruction-based methods tend to have higher complexity and are more likely to blur some details

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  • JPEG image decompression effect method based on DCT coefficient prediction
  • JPEG image decompression effect method based on DCT coefficient prediction
  • JPEG image decompression effect method based on DCT coefficient prediction

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Embodiment Construction

[0018] The JPEG image decompression effect method based on DCT coefficient prediction mainly includes the following steps:

[0019] (1) Given a compressed image, the convolution operation of the network sequentially extracts overlapping image blocks with a size of 8×8 from the upper left corner of the image, and the sliding step of the extraction operation is 6 pixels;

[0020] (2) Perform DCT transformation on each image block through a convolutional neural network to obtain corresponding DCT coefficients;

[0021] (3) Use the wide activation residual network to learn the nonlinear mapping relationship of DCT coefficients to reduce the quantization error;

[0022] (4) Modify the DCT coefficients learned through the network by quantifying the constraints prior to constraining its range to improve the robustness of the network;

[0023] (5) Carry out IDCT transformation to the learned DCT coefficient, transform it back to the pixel domain;

[0024] (6) The output of the netwo...

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Abstract

The invention discloses a JPEG image decompression effect method based on DCT coefficient prediction. The method comprises the following steps: giving a compressed image, and sequentially extracting overlapped image blocks from the top left corner of the image through convolution operation of a network; performing DCT on each image block through a convolutional neural network to obtain a corresponding DCT coefficient; learning a nonlinear mapping relationship of the DCT coefficients by using a wide activation residual network; correcting the DCT coefficient learned through the network throughquantization constraint prior; carrying out IDCT transformation on the learned DCT coefficient by using a network, and transforming the learned DCT coefficient back to a pixel domain; the output of the network is recombined into an image by an inverse operation of the image block extraction operation. According to the JPEG image decompression effect method, the compression effect of the JPEG imagecan be effectively removed, and part of image detail information lost due to compression can be recovered. Therefore, the JPEG image compression effect removing method is an effective JPEG image compression effect removing method.

Description

technical field [0001] The present invention designs a JPEG image decompression effect method based on DCT coefficient prediction. This convolutional neural network based on the DCT domain can better learn the prior knowledge of JPEG compression in the DCT domain, and can maintain image edge detail information. At the same time, better suppression of compression noise belongs to the field of digital image processing. Background technique [0002] In today's era of information explosion, people are exposed to a large amount of information every day, including a lot of image information. In order to save transmission bandwidth and increase the transmission speed of information, image compression technology is often used to compress images, among which JPEG is currently one of the most commonly used image compression methods. However, as people have higher and higher requirements on image clarity, how to improve the definition of compressed images has become an urgent problem ...

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Application Information

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IPC IPC(8): H04N19/625H04N19/86H04N19/117G06N3/04
CPCH04N19/625H04N19/86H04N19/117G06N3/045
Inventor 熊淑华孙梦笛何小海李兴龙任超普拉迪普卡恩滕奇志
Owner SICHUAN UNIV
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