阅读说明：本技术 一种基于加权滤波函数的小波变换和逆变换方法及设备 (Wavelet transformation and inverse transformation method and device based on weighted filtering function ) 是由 王杰林 于 2021-01-06 设计创作，主要内容包括：本发明公开了一种基于加权滤波函数的小波变换和逆变换方法及设备,小波变换和逆变换方法包括,首先获取图像和图像的小波变换/逆变换层数,对图像的每层小波变换/逆变换设置唯一对应的加权滤波函数,然后对图像执行小波变换变换/逆变换,其中每层的小波变换变换/逆变换过程包括：对图像进行小波行列变换,分别将行列变换后的小波系数的高频部分与每层小波变换变换/逆变换对应的加权滤波函数进行乘运算；将图像的高和宽分别进行除2运算。本发明构造了一种加权滤波函数,利用该滤波函数使得小波变换过程中时频分解更充分,从而提升图像的压缩率。而且根据不同图像特征,可定义不同的滤波函数实现图像压缩效果最优。(The invention discloses a wavelet transform and inverse transform method and device based on weighted filter function, the wavelet transform and inverse transform method includes, firstly, obtaining wavelet transform/inverse transform layer number of image and image, setting unique corresponding weighted filter function for each layer of wavelet transform/inverse transform of image, then executing wavelet transform/inverse transform for image, the wavelet transform/inverse transform process of each layer includes: performing wavelet row-column transformation on the image, and respectively multiplying the high-frequency part of the wavelet coefficient subjected to row-column transformation by the weighted filter function corresponding to the wavelet transformation/inverse transformation of each layer; the division by 2 is performed for the height and width of the image, respectively. The invention constructs a weighted filter function, and makes the time-frequency decomposition more sufficient in the wavelet transformation process by using the filter function, thereby improving the compression ratio of the image. And according to different image characteristics, different filter functions can be defined to realize optimal image compression effect.)

1. A wavelet transform method based on a weighted filter function, comprising the steps of:

acquiring an image to be processed and the wavelet transformation layer number of the image;

setting a unique corresponding weighted filter function for each layer of wavelet transform of the image, wherein the value of the weighted filter function is greater than 0 and less than or equal to 1;

performing a wavelet transform on the image, wherein the wavelet transform process for each layer comprises:

performing wavelet line-column transformation on the image, and respectively multiplying the high-frequency part of the wavelet coefficient subjected to line-column transformation by the weighted filter function corresponding to each layer of wavelet transformation; and respectively carrying out division 2 operation on the height and the width of the image.

2. The wavelet transform method based on weighted filtering function of claim 1, wherein the value of said weighted filtering function corresponding to the wavelet transform of the next layer is higher than the value of said weighted filtering function corresponding to the wavelet transform of the previous layer.

3. The wavelet transform method based on weighted filtering function of claim 1, wherein said performing wavelet transform on said image comprises one of:

performing haar wavelet transform on the image;

performing a 5-3 lifting wavelet transform on the image;

performing 9-7 lifting wavelet transform on the image.

4. A wavelet transform method based on weighted filtering function according to any one of claims 1 to 3, characterized by further comprising the steps of:

and compressing the image after the wavelet transformation is finished by adopting a multilevel tree set splitting method.

5. A wavelet inverse transform method based on a weighted filter function, comprising the steps of:

acquiring an image to be processed and the number of layers of wavelet inverse transformation of the image;

setting a unique corresponding weighted filter function for each layer of inverse wavelet transform of the image, wherein the value of the weighted filter function is greater than 0 and less than or equal to 1;

performing an inverse wavelet transform on the image, wherein the inverse wavelet transform process for each layer comprises:

performing wavelet column-row inverse transformation on the image, and performing division operation on the high-frequency part of the wavelet coefficient subjected to column-row inverse transformation and the weighted filtering function corresponding to each layer of wavelet inverse transformation; and respectively multiplying the height and the width of the image by 2.

6. The inverse wavelet transform method based on weighted filter function according to claim 5, wherein the value of said weighted filter function corresponding to the next layer inverse wavelet transform is higher than the value of said weighted filter function corresponding to the previous layer inverse wavelet transform.

7. The weighted filter function-based inverse wavelet transform method of claim 5, wherein said performing an inverse wavelet transform on said image comprises one of:

performing a haar inverse wavelet transform on the image;

performing a 5-3 lifting wavelet inverse transform on the image;

performing 9-7 lifting wavelet inverse transforms on the images.

8. The weighted filter function-based inverse wavelet transform method according to any one of claims 5 to 7, further comprising the steps of:

and compressing the image after the wavelet inverse transformation is completed by adopting a multilevel tree set splitting method.

9. A wavelet transform and inverse transform device based on a weighted filter function, comprising: at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a weighted filter function based wavelet transform method as claimed in any one of claims 1 to 4 and/or a weighted filter function based wavelet inverse transform method as claimed in any one of claims 5 to 8.

10. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the weighted filter function-based wavelet transform method according to any one of claims 1 to 4 and/or the weighted filter function-based wavelet inverse transform method according to any one of claims 5 to 8.

Technical Field

The invention relates to the technical field of image or video processing, in particular to a wavelet transform and inverse transform method and device based on a weighted filter function.

Background

JPEG2000 and H266 are image, video compression standards based on wavelet theory. Wavelet transform is widely applied to the fields of image video, audio compression, image recognition, noise reduction and the like. However, wavelet transformation has the problems of large computation amount, incomplete filtering due to different image, video and audio data, low compression ratio, unreasonable noise reduction and the like.

Disclosure of Invention

The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a wavelet transform and inverse transform method and device based on a weighted filter function, which can improve the compression rate of an image.

In a first aspect of the present invention, a wavelet transform method based on a weighted filter function is provided, which includes the following steps:

acquiring an image to be processed and the wavelet transformation layer number of the image;

setting a unique corresponding weighted filter function for each layer of wavelet transform of the image, wherein the value of the weighted filter function is greater than 0 and less than or equal to 1;

performing a wavelet transform on the image, wherein the wavelet transform process for each layer comprises:

performing wavelet line-column transformation on the image, and respectively multiplying the high-frequency part of the wavelet coefficient subjected to line-column transformation by the weighted filter function corresponding to each layer of wavelet transformation; and respectively carrying out division 2 operation on the height and the width of the image.

According to the embodiment of the invention, at least the following beneficial effects are achieved:

the embodiment of the invention constructs a weighted filter function, and the weighted filter function is used for participating in the wavelet transformation of the image, so that the time-frequency decomposition in the wavelet transformation process is more sufficient, and the compression rate of the image is improved. And according to different image characteristics, different filter functions can be defined to realize optimal image compression effect.

According to some embodiments of the present invention, a value of the weighted filter function corresponding to a wavelet transform of a next layer is higher than a value of the weighted filter function corresponding to a wavelet transform of an upper layer.

According to some embodiments of the invention, the performing a wavelet transform on the image comprises one of:

performing haar wavelet transform on the image;

performing a 5-3 lifting wavelet transform on the image;

performing 9-7 lifting wavelet transform on the image.

According to some embodiments of the invention, further comprising the step of: and compressing the image after the wavelet transformation is finished by adopting a multilevel tree set splitting method.

In a second aspect of the present invention, there is provided a wavelet inverse transformation method based on a weighted filter function, comprising the steps of:

acquiring an image to be processed and the number of layers of wavelet inverse transformation of the image;

setting a unique corresponding weighted filter function for each layer of inverse wavelet transform of the image, wherein the value of the weighted filter function is greater than 0 and less than or equal to 1;

performing an inverse wavelet transform on the image, wherein the inverse wavelet transform process for each layer comprises:

performing wavelet column-row inverse transformation on the image, and performing division operation on the high-frequency part of the wavelet coefficient subjected to column-row inverse transformation and the weighted filtering function corresponding to each layer of wavelet inverse transformation; and respectively multiplying the height and the width of the image by 2.

According to the embodiment of the invention, at least the following beneficial effects are achieved:

the embodiment of the invention constructs a weighted filter function, and the weighted filter function is used for participating in the wavelet transformation of the image, so that the time-frequency decomposition in the wavelet transformation process is more sufficient, and the compression rate of the image is improved. And according to different image characteristics, different filter functions can be defined to realize optimal image compression effect.

According to some embodiments of the invention, the value of the weighted filter function corresponding to the next layer of inverse wavelet transform is higher than the value of the weighted filter function corresponding to the previous layer of inverse wavelet transform.

According to some embodiments of the invention, the performing an inverse wavelet transform on the image comprises one of:

performing a haar inverse wavelet transform on the image;

performing a 5-3 lifting wavelet inverse transform on the image;

performing 9-7 lifting wavelet inverse transforms on the images.

According to some embodiments of the invention, further comprising the step of: and compressing the image after the wavelet inverse transformation is completed by adopting a multilevel tree set splitting method.

In a third aspect of the present invention, there is provided a wavelet transform and inverse transform device based on a weighted filter function, comprising: at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a weighted filter function based wavelet transform method according to the first aspect of the invention and a weighted filter function based wavelet inverse transform method according to the second aspect of the invention.

In a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform a weighted filter function-based wavelet transform method according to the first aspect of the present invention and a weighted filter function-based wavelet inverse transform method according to the second aspect of the present invention.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a schematic diagram of a two-dimensional wavelet transform with 3 wavelet transform layers according to an embodiment of the present invention;

fig. 2 is a flowchart illustrating a wavelet transformation method based on a weighted filter function according to a first embodiment of the present invention;

fig. 3 is a flowchart illustrating a wavelet inverse transformation method based on a weighted filtering function according to a second embodiment of the present invention;

fig. 4 is a flowchart illustrating a wavelet transformation method based on a weighted filtering function according to a third embodiment of the present invention;

fig. 5 is a flowchart illustrating a wavelet inverse transformation method based on a weighted filtering function according to a fourth embodiment of the present invention;

FIG. 6 shows an example of compression of an image according to a fifth embodiment of the present invention;

FIG. 7 shows an example of compression of an image according to a fifth embodiment of the present invention;

fig. 8 is a schematic structural diagram of a wavelet transform and inverse transform device based on a weighted filtering function according to a sixth embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

Before the embodiments of the present invention are introduced, the principle of the present invention will be explained:

JPEG2000 and H266 are image, video compression standards based on wavelet theory. Wavelet transform is widely applied to the fields of image video, audio compression, image recognition, noise reduction and the like at present. However, the wavelet operation amount is large, so lifting algorithms such as 5-3 lifting wavelets and 9-7 lifting wavelets are developed. The Laurent polynomial Euclidean algorithm, Daubechies and Sweldens research is utilized to provide the theorem of multi-phase matrix factorization, and the theorem forms the basis of wavelet transformation lifting implementation.

Theorem 1.1: if the determinant of p (z) is equal to 1, i.e. detp (z) is 1, there is always a Laurent polynomial u_i(z) and p_i(z) (1 ≦ i ≦ m) and a non-zero constant K such that:

wherein p is_m(z) ═ 0, z represents the z domain. A time series f (n) by its sequence numberWhen n is a Roman series generated by power generation of z, and is called z … 1 of the time series, z is e^-iωThe figure is a unit circle with a radius of 1 and an argument of ω. If the convergence field comprises a unit circle, then the z-transform can be written as this is a discrete time fourier transform (fourier transform).

Based on theorem 1.1, lifting realizations of 5-3 lifting wavelet transform and 9-7 lifting wavelet transform can be obtained. The polyphase matrix factorization of the lifting wavelet filter, as 5-3, can yield:

wherein tau is-0.5, nu is 0.25,similarly, a lifting implementation method of 9-7 lifting wavelet transform can be obtained, which is not described herein.

From the above, the wavelet improves the transformation and inverse transformation efficiency, and cannot improve the time-frequency decomposition degree. The time-frequency decomposition of the image is more sufficient because different image types adopt different wavelet functions and scale functions. Therefore, the invention constructs a weighted filter function, and makes the time-frequency decomposition more sufficient in the process of wavelet transformation by using the filter function, thereby improving the compression rate of the image. And according to different image characteristics, different filter functions can be defined to realize optimal image compression effect.

There are many common filtering methods, such as a clipping filtering method, a median filtering method, an arithmetic mean filtering method, a recursive mean filtering method, a kalman filtering method, and the like. In practical application, in the process of image wavelet decomposition, the two-dimensional wavelet transformation is a row-column-based iterative operation process, and the problem of filter divergence still exists in the wavelet transformation. When the weighted filtering method is adopted for long-time recursive calculation under a large-size image, filtering divergence cannot be caused. And a weighted filtering method is introduced in the row-column iterative operation of the two-dimensional wavelet transformation, and the method mainly aims at solving the detail value (high-frequency value) v in the process of wavelet transformation of each layer_i,j(wherein i, j is a two-dimensional numberRow column index) of the filtered valuesAnd (3) gradually weakening the action of detail values far away from the current moment (i, j), thereby realizing more sufficient time-frequency decomposition.

The standard-based Haar wavelet is provided below, the implementation principle of the present invention:

based on the Haar wavelet of the standard, let { x₁,x₂Is a signal composed of two elements, defined as the average and detail of:

then { a, d } can be used as another representation of the signal, and { x }₁,x₂There may be a recovery from { a, b } as follows:

x₁＝a+b,x₂＝a-b (4)

of course, { a, b } → { c, d } (meaning that { a, b } is wavelet transformed into { c, d }) may be redefined, such asd-b-a. Because of the fact thatThen d is replaced by b, then b-a; replacing c with a, thenObviously, in order to implement the wavelet transform in situ (in-place), i.e., { a, b } → { a, b }, the wavelet transform (5) and the inverse transform (6) may be performed in the following manner:

b-＝a；a+＝b/2 (5)

a-＝b/2；b+＝a (6)

then expand the element to m, then { x₁,x₂,…,x_mAnd expanding the wavelet function to a multi-resolution condition to obtain different wavelet functions. Assume that there is a weight coefficient r such that:

it can be found that:

when r is known, and 0<r<1, due to r (b-a)<(b-a) therefore, the detail (high frequency) portion values become small, while the average information c does not change, and the inverse wavelet can be lossless by equation (9). The purpose of weighting is to make the image have r as two-dimensional transformation<1, so after line iteration computation | d | → 0. The detail (high frequency) part information obtains less bit number and scanning times when the bit plane is scanned, thereby improving the compression ratio. And (8) and (9) are lossless wavelet transform processes in a real number domain, so that the image quality is unchanged. As in fig. 1, block a belongs to high frequency data, followed by B and C, A, B, C relates only to r. And D, E, F may define new weight coefficients in addition to those related to r. Thus, a first level wavelet transform r is defined₁The second layer is r₂Analogically the weighted filter function r can be obtained_levelF (level), where { r }₁,r₂,…,r_levelGiven according to the image characteristics.

From the formula (8)Obviously, the low-frequency part is amplified, and the edges of all color blocks in the image belong to low-frequency information, so that the weighted edges are beneficial to obtaining more bits when the bit plane is scanned, and the weighted edges have important significance in the applications of image analysis and identification, boundary finding, component analysis, sharpening and the like.

When r >1, the details (high frequency) are magnified, while the low frequency data are reduced and the color block boundaries are faded. The method can be used for noise reduction processing of image or audio data.

Based on the weighted filter function f (level), different filter functions can be constructed according to different data characteristics, and a new filter is superposed during wavelet transformation when 0< f (level) is less than or equal to 1. Better compression methods are constructed in the image, video and audio domains.

A first embodiment;

referring to fig. 2, an embodiment of the present invention provides a wavelet transform method based on a weighted filter function, including the following steps:

s101, acquiring an image to be processed and the wavelet transformation layer number of the image.

S102, setting a unique corresponding weighted filter function for each layer of wavelet transform of the image, wherein the value of the weighted filter function is greater than 0 and less than or equal to 1.

S103, performing wavelet transformation on the image, wherein the wavelet transformation process of each layer comprises the following steps: performing wavelet line-column transformation on the image, and respectively multiplying the high-frequency part of the wavelet coefficient subjected to line-column transformation by the weighted filter function corresponding to each layer of wavelet transformation; the division by 2 is performed for the height and width of the image, respectively.

In this step S102, the weighting filter function uniquely corresponding to each wavelet transform setting of the image may satisfy (0, 1)]The value range (only less than 1 can the filter be superposed, otherwise, the wavelet signal amplifier) of (1) is set by self-definition. For example, the wavelet transform level is 3, and the weighted filter functions defining different layers are respectively: r is₁＝0.8，r₂＝0.9，r₃0.95 or r₁＝0.8，r₂＝0.8，r₃0.8 or r₁＝0.95，r₂＝0.9，r₃0.8. As an alternative implementation, this embodiment sets the value of the weighting filter function corresponding to the wavelet transform of the next layer higher than the value of the weighting filter function corresponding to the wavelet transform of the previous layer according to the wavelet transform layer number of the image, for example: 3, defining the wavelet transform level as 3, 1 as the mark of the current layer, and self-defining the weighted filter function r of different layers₁＝0.8，r₂＝0.9，r₃0.95. Compared with the setting of other weighting filter functions, the experimental result obtained by the design is optimal.

In step S103, let the current layer identifier be l, in the process of wavelet transform of each layer of the image, wavelet line transform is first performed, as an optional implementation, where the wavelet line transform may be a haar wavelet, or a 5-3 lifting wavelet, or a 9-7 lifting wavelet, and the present embodiment is not limited. After the wavelet line transformation, the high frequency part of the line-transformed wavelet coefficients is weighted, i.e. the wavelet coefficients are weightedWavelet coefficient xi of_iPerforming a weighting operation, ξ_i＝r_lξ_i. Then wavelet column transformation is performed, and after wavelet column transformation, the high frequency part of the wavelet coefficients after column transformation is weighted, i.e. the wavelet coefficients are weightedWavelet coefficient epsilon_j＝r_lε_j. Finally, the height and width of the image are respectively divided by 2, namelyw is the width of the image and h is the height of the image.

The beneficial effects that this embodiment possesses include:

the embodiment of the invention constructs a weighted filter function, and the weighted filter function is used for participating in the wavelet transformation of the image, so that the time-frequency decomposition in the wavelet transformation process is more sufficient, and the compression rate of the image is improved. And according to different image characteristics, different filter functions can be defined to realize optimal image compression effect.

As an optional implementation manner, after step S103, the method further includes the steps of:

and S104, compressing the image after the wavelet transformation is finished by adopting a multi-level tree set splitting method (SPIHT). The compression ratio of SPIHT is higher than that of EZW and EBCOT algorithms, and the following experimental results can be seen.

A second embodiment;

referring to fig. 3, an embodiment of the present invention provides a wavelet inverse transformation method based on a weighted filter function, including the following steps:

s201, obtaining an image to be processed and the number of layers of the wavelet inverse transformation of the image.

S202, setting a unique corresponding weighted filter function for each layer of inverse wavelet transform of the image, wherein the value of the weighted filter function is greater than 0 and less than or equal to 1.

S203, performing wavelet inverse transformation on the image, wherein the wavelet inverse transformation process of each layer comprises the following steps: performing wavelet column-row inverse transformation on the image, and performing division operation on the high-frequency part of the wavelet coefficient subjected to column-row inverse transformation and the weighted filtering function corresponding to each layer of wavelet inverse transformation; the height and width of the image are multiplied by 2, respectively.

And S204, compressing the image after the wavelet inverse transformation is completed by adopting a multi-level tree set splitting method (SPIHT).

For example: 3, defining the weight coefficient r of different layers by using the identifier l of the current layer as 1_l＝1＝0.8，r_l＝2＝0.9，r_l＝30.95. w is the width of the imageh is the height of the image

In step S203, in the process of inverse wavelet transform of each layer of the image, wavelet column transform is first performed, as an alternative embodiment, the wavelet column transform may be a haar wavelet, or a 5-3 lifting wavelet, or a 9-7 lifting wavelet, and the present embodiment is not limited. After the wavelet column transform is performed, the high frequency part of the column transformed wavelet coefficients is weighted, i.e.Wavelet coefficient epsilon_jTo addThe operation of the weights is carried out,then a wavelet line transformation is performed, after which the high frequency part of the line transformed wavelet coefficients is weighted, i.e. the wavelet coefficients are weightedWavelet coefficient xi of_iThe weight calculation is carried out to carry out the weight calculation,and finally, multiplying the height and the width of the image by 2 respectively, namely w ═ w × 2 and h ═ h × 2.

The beneficial effects that this embodiment possesses include:

the embodiment of the invention constructs a weighted filter function, and the weighted filter function is used for participating in the wavelet inverse transformation of the image, so that the time-frequency decomposition in the wavelet inverse transformation process is more sufficient, and the compression rate of the image is improved. And according to different image characteristics, different filter functions can be defined to realize optimal image compression effect.

A third embodiment;

referring to fig. 4, in order to facilitate those skilled in the art to understand the contents of the first embodiment of the present invention, an embodiment is provided, in which a wavelet transform method based on a weighted filter function includes the following steps:

s301, initializing parameters, wherein the wavelet transform level is 3, the identifier of the current layer is l, and self-defining the weight coefficient r of different layers_l＝1＝0.8，r_l＝2＝0.9，r_l＝30.95, w is the width of the image and h is the height of the image.

And S302, performing wavelet line transformation.

This embodiment takes a 5-3 lifting wavelet as an example.

S303, weighting the high frequency part of the wavelet coefficient after line transformation, i.e.Wavelet coefficient xi of_iPerforming a weighting operation, ξ_i＝r_iξ_i。

S304, then carrying out lifting wavelet column transformation.

S305, weighting the high frequency part of the wavelet coefficient after column transformation, i.e.Wavelet coefficient epsilon_j＝r_lε_j。

S306, l is l +1, if l is less than or equal to level, thenSkipping to step S302; if l>level, ending the wavelet transformation.

And finally, compressing the image by adopting a multi-level tree set Splitting (SPIHT) method.

The embodiment of the invention constructs a weighted filter function, and the weighted filter function is used for participating in the wavelet transformation of the image, so that the time-frequency decomposition in the wavelet transformation process is more sufficient, and the compression rate of the image is improved. And according to different image characteristics, different filter functions can be defined to realize optimal image compression effect. The image compression ratio obtained by the method is higher than that of the traditional wavelet transform, and the method can be used for image, video and audio compression in the future and can also be used in the fields of analysis, identification and the like.

A fourth embodiment;

referring to fig. 5, in order for those skilled in the art to understand the contents of the second embodiment of the present invention, an embodiment is provided, in which a wavelet inverse transformation method based on a weighted filter function includes the following steps:

s401, initializing parameters, wherein the wavelet transform level is 3, the identifier l of the current layer is 3, and self-defining the weight coefficient r of different layers_l＝1＝0.8，r_l＝2＝0.9，r_l＝30.95, w is the width of the imageh is the height of the image

S402, wavelet column inverse transformation is carried out.

This embodiment takes a 5-3 lifting wavelet as an example.

S403, inverse weighting the high frequency part of the wavelet coefficient after column inverse transformation, i.e.Wavelet coefficient of

S404, performing wavelet line inverse transformation.

S405, inverse weighting the high frequency part of the wavelet coefficient after line inverse transformation, i.e.Wavelet coefficient xi of_jThe weight calculation is carried out to carry out the weight calculation,

s406, l-1, if l is less than or equal to level, w is 2, h is 2, and go to step S402; and if l is greater than level, finishing the wavelet inverse transformation.

And finally, compressing the image by adopting a multi-level tree set Splitting (SPIHT) method.

The embodiment of the invention constructs a weighted filter function, and the weighted filter function is used for participating in the wavelet inverse transformation of the image, so that the time-frequency decomposition in the wavelet inverse transformation process is more sufficient, and the compression rate of the image is improved. And according to different image characteristics, different filter functions can be defined to realize optimal image compression effect. The image compression ratio obtained by the method is higher than that of the traditional wavelet transform, and the method can be used for image, video and audio compression in the future and can also be used in the fields of analysis, identification and the like.

A fifth embodiment;

to demonstrate the gap in performance between the present invention and the existing wavelet transform, this example provides several sets of experimental data:

three experiments were performedScenes are one hundred pictures (BMP images) of each of the Linda pattern, the monitoring image, and the landscape image, and each has a size w of 1920 and h of 1080. Based on the 9-7 lifting wavelet transform, the image is decomposed by the method described in the first embodiment, and then compressed by using a multi-level tree set Splitting (SPIHT) algorithm. Under the condition of same PSNR (PSNR is more than or equal to 40) and same wavelet transform level being 3, when { r is equal to 3_l＝1＝0.9,r_l＝2＝0.95,r_i＝31, the average compression ratio is improved by 11.2 percent; when { r_l＝1＝0.8,r_l＝2＝0.9,r_l＝30.95), the average compression ratio is improved by 14.7%; when { r_l＝1＝0.15,r_l＝2＝0.7,r_l＝3When the average compression ratio is 0.9, the average compression ratio is improved by 23 percent.

For 4K and 8K images, the level is 7, and after constructing a weighted filter function, the compression gain of 30% or more is obtained through experiments.

Fig. 6 and 7 each show an example of image compression.

A sixth embodiment;

referring to fig. 8, an embodiment of the present invention provides a wavelet transform and inverse transform device based on a weighted filter function, which may be any type of smart terminal, such as a mobile phone, a tablet computer, a personal computer, etc. Specifically, the weighted filter function-based wavelet transform and inverse transform apparatus includes: one or more control processors and memory, here exemplified by a control processor. The control processor and the memory may be connected by a bus or other means, here exemplified by a connection via a bus.

The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the weighted filter function based wavelet transform and inverse transform apparatus in the embodiments of the present invention. The control processor implements the weighted filter function based wavelet transform method or the weighted filter function based wavelet inverse transform method described in the above embodiments by running a non-transitory software program, instructions, and modules stored in the memory.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes a memory remotely located from the control processor, and these remote memories may be connected to the weighted filter function based wavelet transform and inverse transform device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory and, when executed by the one or more control processors, perform the weighted filter function based wavelet transform method or the weighted filter function based wavelet inverse transform method described in the above embodiments.

The present invention also provides a computer-readable storage medium having stored thereon computer-executable instructions for executing, by one or more control processors, the weighted filter function-based wavelet transform method or the weighted filter function-based wavelet inverse transform method described in the above embodiments.

Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes in the methods for implementing the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes in the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.