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Image compression method based on direction lifting wavelet and improved SPIHT under IoT (Internet of Things)

A technology for image compression and the Internet of Things, applied in image communication, digital video signal modification, electrical components, etc., can solve problems such as low coding efficiency and inability to retain image details, achieve good coding performance, improve subjective quality, and low complexity Effect

Inactive Publication Date: 2018-11-13
QIQIHAR UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that the existing SPIHT method rarely considers the edge blur or ringing effect caused by the lack of high-frequency information, and cannot retain more details in the image, resulting in low coding efficiency. Image Compression Method Based on Direction Lifting Wavelet and Improved SPIHT

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  • Image compression method based on direction lifting wavelet and improved SPIHT under IoT (Internet of Things)
  • Image compression method based on direction lifting wavelet and improved SPIHT under IoT (Internet of Things)
  • Image compression method based on direction lifting wavelet and improved SPIHT under IoT (Internet of Things)

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specific Embodiment approach 1

[0054] Specific implementation mode one: the specific process of the image compression method based on direction-lifting wavelet and improved SPIHT under the Internet of Things in this embodiment is as follows:

[0055] Step 1, performing image block segmentation on the remote sensing image to obtain segmented image blocks;

[0056] Step 2, calculating the best prediction direction for the segmented image blocks respectively, to obtain the best prediction direction of the segmented image blocks;

[0057] Step 3. By calculating weighted directional interpolation filter coefficients, weighted directional interpolation is performed on the fractional sample values ​​needed in the direction boosting process to obtain interpolated image blocks;

[0058] Step 4, using the best prediction direction obtained in step 2, respectively carry out wavelet transformation based on direction lifting to the interpolated image block, and obtain each transformed image block, that is, each transfor...

specific Embodiment approach 2

[0061]Specific embodiment 2: the difference between this embodiment and specific embodiment 1 is that in the step 1, the remote sensing image is segmented into image blocks to obtain the segmented image blocks; the specific process is:

[0062] In order to make the lifting direction consistent with the local texture direction of the image, image segmentation is performed first. In [19], a quadtree-based rate-distortion optimization segmentation method is adopted. However, the efficiency of this segmentation method is closely related to the image content. For some image types, such as remote sensing images, which usually reflect complex landforms, the detailed information is usually rich, and there are few large flat areas. At this time, it is difficult for the adaptive segmentation method to show its advantages. The reason is that for images with complex content, the result of using the adaptive segmentation method is very likely that almost all blocks are the smallest blocks...

specific Embodiment approach 3

[0091] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that in the step two, the best prediction direction is calculated respectively for the divided image block, and the best prediction direction of the divided image block is obtained; the specific process for:

[0092] For wavelet transform based on direction lifting, the prediction error and high frequency subband are closely related. The larger the prediction error, the more information in the high-frequency subbands, and the lower the coding performance. For an image block, its optimal prediction direction should be the direction that can minimize the residual information of the high-frequency sub-band.

[0093] The process of calculating the best prediction direction of an image block is as follows: Figure 4 as shown,

[0094] Suppose the reference direction set is θ ref , the reference direction set θ refContains 15 reference directions, which are recorded as...

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Abstract

The invention relates to an image compression method based on a direction lifting wavelet and improved SPIHT under the IoT (Internet of Things). The invention discloses an image compression method. The invention aims to solve a problem of low encoding efficiency caused by a case that an existing SPIHT method rarely considers edge blurs or a ringing effect caused by missing of high-frequency information and cannot retain more details in an image. The image compression method comprises the process of: 1, obtaining partitioned image blocks; 2, obtaining an optimal prediction direction of the partitioned image blocks; 3, carrying out weighting direction interpolation on a score sample value by calculating a weighting direction interpolation filter coefficient so as to obtain interpolated imageblocks; 4, respectively carrying out wavelet transform based on direction lifting on the interpolated image blocks by utilizing the optimal prediction direction so as to obtain each transformed imageblock; 5, forming an integral transformed image by all the transformed image blocks; and 6, by utilizing an improved SPIHT method, carrying out encoding on the transformed image obtained in the step5 so as to obtain the encoded image. The image compression method is used for the field of image compression.

Description

technical field [0001] The invention relates to an image compression method. Background technique [0002] Due to the tremendous progress in computing technology and sensor technology in recent years, the Internet of things (IoT) has also entered a period of rapid development [1] (Sezer OB, DogduE, Ozbayoglu AM (2018) Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey. IEEE Internet of Things Journal 5(1): 1-27. http: / / dx.doi.org / 10.1109 / JIOT.2017.2773600). In the sense of IoT, "things" refer to a wide range of devices, such as heart monitoring equipment, temperature measurement equipment, and automatic cars, etc. [2-3] ([2] XuL D., HeW, LiS (2014) Internet of things in industries: a survey. IEEE Transactions on Industrial Informatics 10(4):2233-2243. http: / / dx.doi.org / 10.1109 / TII.2014.2300753 [3] Iqbal M M, Farhan M, Jabbar S, et al (2018) Multimedia based IoT-centric smart framework for eLearning paradigm. Multimed Tools Appl1-20. https: / / ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N19/117H04N19/176H04N19/42H04N19/63H04N19/96H04N19/80
CPCH04N19/117H04N19/176H04N19/42H04N19/63H04N19/80H04N19/96
Inventor 石翠萍靳展何鹏朱恒军李静辉那与晶潘悦
Owner QIQIHAR UNIVERSITY
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