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JPEG image compression artifact elimination algorithm based on cascade residual coding and decoding network

An image compression, encoding and decoding technology, applied in the field of image processing, can solve problems such as limiting the application range of algorithms, and achieve good performance

Pending Publication Date: 2021-03-16
XI AN JIAOTONG UNIV
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Problems solved by technology

At present, most algorithms need to predict the coding information of the compressed image, which limits the application range of the algorithm

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  • JPEG image compression artifact elimination algorithm based on cascade residual coding and decoding network
  • JPEG image compression artifact elimination algorithm based on cascade residual coding and decoding network
  • JPEG image compression artifact elimination algorithm based on cascade residual coding and decoding network

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Embodiment Construction

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Such as figure 1 As shown, a kind of JPEG image compression artifact elimination algorithm based on cascaded residual codec network of the present invention comprises the following steps:

[0037] Step 1: Divide the test image into a series of overlapping image blocks, estimate the QF value of each image block through the QF prediction network, and use the local standard deviation (LSD) to locate the top 20% to 50% of each image block The rounded mean of the QF values ​​is taken as the overall QF value of the test image.

[0038] The structure of the QF prediction network is shown in Table 1. In the training phase, use the 45,000 images of the MS-COCO database training set to train the QF value prediction network. The specific method is: use a random integer value (ie QF) for the brightness channel of each image (ie, the Y channel in the YCbCr space) For comp...

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Abstract

The invention discloses a JPEG image compression artifact elimination algorithm based on a cascade residual error coding and decoding network CRED-Net, which comprises the following steps of: firstly,providing QF value estimation of a compressed image by using a quality factor (QF) prediction network, and then removing an appropriate cascade of artifacts of the compressed image by selecting an appropriate QF value for the estimated QF value. According to the method provided by the invention, multi-scale feature learning is carried out in a pixel domain, a discrete cosine transform (DCT) domain and a discrete wavelet transform (DWT) domain without depending on compressed encoding information of the JPEG image, and when the QF value of the image is not within the consideration range of a trained CREDNet model, a correction network is introduced to further correct the restored image. Experimental results prove that compared with other methods. The method provided by the invention can obtain better performance in a wider compression level range.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a JPEG image compression artifact elimination algorithm based on a cascaded residual encoder-decoder network (CRED-Net). Background technique [0002] In recent years, the rapid development of digital imaging technology provides an important basis for image capture, storage and sharing. In order to save bandwidth and device resources, lossy compression methods such as JPEG compression have been widely used in image transmission and storage. However, lossy compression can introduce image artifacts such as blocking artifacts, ringing effects, and blurring, which have a large negative impact on various image processing and computer vision tasks that take compressed images as input. Designing an algorithm that can effectively remove image compression artifacts has become an important research topic in the field of computer vision. At present, most algorithms need to pred...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00G06T9/00
CPCG06T11/008G06T9/00
Inventor 张译禹冬晔牟轩沁
Owner XI AN JIAOTONG UNIV
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