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Sparse representation-based deblocking method

A deblocking, sparse representation technology, applied in the field of image processing, can solve the problem of high computational complexity, achieve the effect of low computational complexity, good visual effect, and high peak signal-to-noise ratio

Inactive Publication Date: 2011-06-08
XIDIAN UNIV
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Problems solved by technology

In addition, there is a method based on the maximum posterior probability, which can effectively reduce the block effect, but the computational complexity is high

Method used

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Embodiment Construction

[0019] refer to figure 1 , the implementation steps of the present invention are expressed as follows:

[0020] Step 1. Train a general dictionary.

[0021] For a clean natural image set, select n 8*8 image blocks, 50000<n<150000, in the simulation experiment, select n to be 100000, construct a training matrix X1 with a size of 64*n, set the number of iterations to 20, use The DCT dictionary whose size is 64*512 carries out dictionary initialization, sets the sparsity S=6 of image block, utilizes KSVD algorithm and batch processing orthogonal matching pursuit algorithm to train general dictionary D according to formula (1),

[0022] min D , Φ | X 1 - D * Φ | F 2 s . t . | ...

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Abstract

The invention discloses a sparse representation-based deblocking method, which mainly solves the problem of the presence of a blocking effect in a block discrete cosine transform (BDCT) compressed image. The method comprises the following implementation steps of: (1) selecting a clean training image set and training a general dictionary with a kernel singular value decomposition (KSVD) algorithm and a batch processing orthogonal matching pursuit algorithm; (2) compressing a test image by controlling a quality factor during joint photographic experts group (JPEG) compression so as to obtain a JPEG compressed image; (3) calculating the noise standard deviation of the JPEG compressed image; (4) automatically estimating an error threshold according to the quality factor and the noise standarddeviation; (5) constructing an image block matrix of the JPEG compressed image so as to obtain a de-noised sparse representation matrix; and (6) obtaining a deblocking result image by using the general dictionary and the sparse representation matrix. Compared with the prior art, the invention has the advantages that: a higher or similar peak signal to noise ratio can be obtained, the visual effect of a deblocked image is good, computation complexity is low, and a blocking effect in a BDCT compressed image can be eliminated.

Description

technical field [0001] The invention belongs to the field of image processing and relates to a machine learning method, in particular to a dictionary learning and threshold automatic estimation method, which can be used to reduce block effect in block discrete cosine transform (BDCT) compressed images. Background technique [0002] The block discrete cosine transform (BDCT) is widely used in image and video compression, and has been adopted by most image and video compression standards, including JPEG, MPEG, H.26X, AVC, etc. In BDCT-based compression coding, the image is first divided into many 8*8 non-overlapping image blocks, and discrete cosine transform is performed on each block to obtain discrete cosine coefficients, which are then quantized and variable-length coded. Data loss will be caused during the quantization process, resulting in distortion of the compressed image, that is, coding effects, including block effects and ringing effects. JPEG compresses digital im...

Claims

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Application Information

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IPC IPC(8): H04N7/26H04N7/30G06N5/04H04N19/86
Inventor 郑喆坤焦李成齐宏涛王爽尚荣华马文萍公茂果马晶晶侯彪
Owner XIDIAN UNIV
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