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Joint optimization training method for image sparse representation multi-dictionary learning

A technique of dictionary learning and joint optimization, applied in image coding, image data processing, instruments, etc., can solve problems such as limited adaptability and inability to compress images, and achieve good objective image quality

Active Publication Date: 2019-08-02
TSINGHUA UNIV +1
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Problems solved by technology

However, this method has limited adaptability and cannot compress images other than faces.

Method used

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  • Joint optimization training method for image sparse representation multi-dictionary learning
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  • Joint optimization training method for image sparse representation multi-dictionary learning

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Experimental program
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Embodiment Construction

[0100] Select Berkeley Segmentation Image database as the training image set, and randomly select 8×10 of 200 images 4 Two image blocks are used as the training set, and the size of each image block is 8×8. The test images come from the USC-SIPI data set, including some standard images, such as Lena, boat, man, couple, camera man, woman, etc. The size of the trained dictionary is 200 dimensions, that is, each dictionary has 200 atoms. The distribution of the energy difference parameter of the image block. Find the gradient of the test image block, and count the energy distribution in the gradient direction of each image block, that is, the energy difference between the two main gradient directions. The probability distribution function is as attached to the specification. figure 2 Shown. The shared dictionaries and some specialized dictionaries obtained through training are shown in the instructions attached. image 3 in.

[0101] The experimental parameters are shown in Table...

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Abstract

The invention provides a combined optimizing and training method for the multi-dictionary learning of image sparse representation, which belongs to the multi-media communication and image data processing field, and is characterized by: regarding the non-zero element in the singular value matrix after the singular value decomposition of the gradient matrix of the training image unit as the energy value of the gradient direction; according to set the energy value parameter threshold, dividing the image unit into various isotropic images and various anisotropic images; learning successively a shared dictionary and a specialized dictionary; optimizing the factors to minimize the objective function such as the residuals of the isotropic and anisotropic images after the sparse representation, the autocorrelation and the degree of cross-correlation of each dictionary, and the regularization of nonzero element; in the optimization process, using the orthogonal matching and tracking algorithm to optimize A0 and Ak, and then optimizing D0 and Dk by the gradient descent algorithm; and in keeping the parameters to be optimized, regarding the items not involving the parameters as constants. When the method is used for image compression, the details could be well preserved, a low distortion rate is achieved and a good image quality could also be achieved.

Description

Technical field [0001] The present invention provides an image data compression method, which belongs to the cross-field of multimedia communication and data compression, and specifically designs an image data compression algorithm for low bit rate, clusters image texture and structured dictionary modeling, and performs image processing Sparse representation is mainly used to reduce the amount of data transmitted during communication. It is not only suitable for images of specific subjects such as human faces, but also for general natural images, and has a wide range of applications. Background technique [0002] Digital multimedia communication is one of the most challenging and fastest growing fields in many fields of current communication technology. The era of big data puts forward higher demands on data compression and transmission. In order to effectively reduce bandwidth pressure and effectively transmit data, image compression has been extensively studied by researchers....

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T9/00
CPCG06T9/005
Inventor 陶晓明黄丹蓝徐迈葛宁陆建华
Owner TSINGHUA UNIV
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