An image compression method, device, and readable storage medium

Abstract

The present invention provides an image compression method, an apparatus, and a readable storage medium, including: performing K-level wavelet transform on an input image to obtain a first wavelet coefficient distribution; determining a common coefficient N in the low-frequency region of the first wavelet coefficient distribution, where the common coefficient N does not exceed the difference obtained by subtracting the maximum absolute value of other regions from the minimum value of the low-frequency region, and the other regions are all non-low-frequency regions in the first wavelet coefficient distribution; subtracting the common coefficient N from each value in the low-frequency region to obtain an updated low-frequency region; the updated low-frequency region and the other regions form a second wavelet coefficient distribution; transmitting the common coefficient; and performing scanning output on the second wavelet coefficient distribution by using an embedded zero-tree wavelet coding algorithm. The present invention can improve the image compression speed and the image compression effect.

Description

Technical Field

[0001] The present invention relates to the field of image compression, and particularly to an image compression method, device, and readable storage medium. Background Art

[0002] Image compression facilitates the efficient storage, management, and transmission of information. Currently, the discrete wavelet transform (DWT) and the Embedded Zerotree Wavelet Coding (EZW) algorithm have been widely applied to image compression processing.

[0003] DWT analyzes image information by transforming it from the time domain to the frequency domain. Through multi-resolution decomposition of the image, information in different spaces and frequencies is obtained. Since there are certain characteristics in the coefficients after wavelet transform, after dividing the coefficients of wavelet transform into parent and child nodes, the EZW algorithm is used to continuously scan the image after wavelet transform to generate more zerotrees for image encoding. The more zerotree roots are generated, the less data volume is used to represent the image.

[0004] The combination of DWT and EZW can effectively improve the compression effect. However, to meet the growing requirements of mobile terminals with limited resources for image processing, it is necessary to further improve the image compression effect. Summary of the Invention

[0005] One of the objectives of the present invention is to overcome at least some of the deficiencies in the prior art, and provide an image compression method, device, and readable storage medium.

[0006] The technical solution provided by the present invention is as follows:

[0007] An image compression method, comprising: performing K-level wavelet transform on an input image to obtain a first wavelet coefficient distribution;

[0008] Determining a common coefficient N in the low-frequency region of the first wavelet coefficient distribution, where the common coefficient N does not exceed the difference obtained by subtracting the maximum absolute value of other regions from the minimum value of the low-frequency region, and the other regions are all non-low-frequency regions in the first wavelet coefficient distribution;

[0009] Subtracting the common coefficient N from each value in the low-frequency region to obtain an updated low-frequency region; the updated low-frequency region and the other regions form a second wavelet coefficient distribution;

[0010] Transmitting the common coefficient N;

[0011] Encoding the second wavelet coefficient distribution using the Embedded Zerotree Wavelet Coding algorithm and transmitting the encoding result.

[0012] In some embodiments, the common coefficient N is equal to the difference obtained by subtracting the maximum value of the absolute values of other regions from the minimum value of the low-frequency region.

[0013] In some embodiments, for a 2 L ×2 M input image, M≥L≥3, set K = L - 1.

[0014] In some embodiments, for a 2 L ×2 M input image, M≥L≥3, set K = L - m, where m > 1.

[0015] In some embodiments, it includes: during decoding, after restoring the second wavelet coefficient distribution, corresponding compensation is performed on each value in the low-frequency region of the second wavelet coefficient distribution according to the received common coefficient.

[0016] The present invention also provides an image compression device, including:

[0017] A wavelet transform module for performing K-level wavelet transform on an input image to obtain a first wavelet coefficient distribution;

[0018] A common coefficient determination module for determining the common coefficient N of the low-frequency region of the first wavelet coefficient distribution, where the common coefficient N does not exceed the difference obtained by subtracting the maximum value of the absolute values of other regions from the minimum value of the low-frequency region, and the other regions are all non-low-frequency regions in the first wavelet coefficient distribution;

[0019] A low-frequency update module for subtracting the common coefficient N from each value in the low-frequency region to obtain an updated low-frequency region; the updated low-frequency region and the other regions form a second wavelet coefficient distribution;

[0020] An encoding and transmission module for transmitting the common coefficient; encoding the second wavelet coefficient distribution using the embedded zero-tree wavelet coding algorithm and transmitting the encoding result.

[0021] In some embodiments, the common coefficient N is equal to the difference obtained by subtracting the maximum value of the absolute values of other regions from the minimum value of the low-frequency region.

[0022] In some embodiments, for a 2 L ×2 M input image, M≥L≥3, set K = L - 1 or K = L - m, where m > 1.

[0023] The present invention also provides an image compression device, including:

[0024] A memory for storing a computer program;

[0025] A processor, which is configured to implement the image compression method described in any of the foregoing embodiments when running the computer program.

[0026] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the image compression method described in any of the foregoing embodiments is implemented.

[0027] The image compression method, device, and readable storage medium provided by the present invention can at least bring the following beneficial effects:

[0028] 1. By subtracting a common coefficient from each value in the low-frequency region, the present invention reduces the magnitude of the values in the low-frequency region, thereby reducing the initial threshold T for scanning, further reducing the number of scans in the embedded zero-tree wavelet coding algorithm, reducing the number of tables to be transmitted, and thus reducing the overall data transmission volume and improving the lossy compression performance of the image.

[0029] 2. By performing one or more fewer wavelet transforms on the image, the present invention can reduce the image compression processing time and improve the image compression efficiency while ensuring the image compression quality. Description of the Drawings

[0030] The following will further illustrate the above characteristics, technical features, advantages, and implementation manners of an image compression method, device, and readable storage medium in a clear and understandable manner in conjunction with the drawings and preferred embodiments.

[0031] Figure 1 is a flowchart of an embodiment of an image compression method of the present invention;

[0032] Figures 2-4 is a schematic diagram of the distribution of wavelet transform coefficients obtained by performing multi-level wavelet transform on an image;

[0033] Figure 5 is a schematic diagram of the main scan process in the EZW algorithm;

[0034] Figure 6 is a schematic structural diagram of an embodiment of an image compression device of the present invention;

[0035] Figure 7 is a schematic structural diagram of another embodiment of an image compression device of the present invention.

[0036] Explanation of the Reference Numerals in the Drawings:

[0037] 100. Wavelet transform module, 200. Common coefficient determination module, 300. Low-frequency update module, 400. Coding and transmission module, 10. Memory, 20. Computer program, 30. Processor. Detailed Embodiments

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will describe the specific embodiments of the present invention with reference to the accompanying drawings. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings and other embodiments can be obtained.

[0039] To make the drawings concise, only the parts related to the present invention are schematically shown in each drawing, and they do not represent the actual structure of the product. In addition, to make the drawings concise and easy to understand, in some drawings, for components with the same structure or function, only one of them is schematically drawn, or only one of them is marked. In this article, "one" not only means "only this one", but also can mean "more than one" situation.

[0040] Traditional image compression methods based on the DWT and EZW algorithms include the following steps:

[0041] 1. Perform discrete wavelet transform on the input image

[0042] Perform K-level wavelet transform on the input image to obtain the wavelet coefficient distribution. K can be determined according to the size of the image. For example, for an 8*8 image, K can be 3; for a 16*16 image, K can be 4.

[0043] Taking an 8*8 image as an example, perform two-dimensional wavelet transform (i.e., the first-level wavelet transform) to form four regions (such as Figure 2 ), where LL1 is the low-frequency region, representing the main feature information of the image; HL1 and LH1 are the mid-frequency regions, representing the secondary feature information of the image; HH1 is the high-frequency region, representing the detailed feature information of the image. Further decompose the information in the low-frequency region LL1, that is, perform two-dimensional wavelet transform (i.e., the second-level wavelet transform) on LL1 alone to obtain the wavelet coefficient distribution as shown in Figure 3 . Then further decompose the information in the LL2 region, that is, perform two-dimensional wavelet transform (i.e., the third-level wavelet transform) on it alone to obtain the wavelet coefficient distribution as shown in Figure 4 . Through multi-level wavelet transform, frequency decomposition of the image at different levels can be obtained. Among them, LLx (x = 1, 2, 3) is the low-frequency region, and the others are non-low-frequency regions. The values in the low-frequency region are positive, and there may be negative values in other regions.

[0044] 2. Use the EZW algorithm to encode and output the wavelet coefficient distribution

[0045] Use the EZW algorithm to perform multiple scans and output on the wavelet coefficient distribution. The scanning order for each time can be as shown in Figure 4 .

[0046] The EZW algorithm includes multiple scans of the wavelet coefficient distribution. Each scan consists of two stages: the main scan and the secondary scan. The main scan scans the wavelet coefficient distribution to generate significant coefficients and tree information, and the secondary scan scans the significant coefficients to encode the values of the significant coefficients for reconstructing the significant coefficient values during decoding. It includes:

[0047] (1) Determine the initial threshold T

[0048] The initial threshold for the scan is T, and its formula is as follows:

[0049]

[0050] where max is the wavelet coefficient with the largest absolute value after wavelet transform, represents the largest integer not greater than x.

[0051] The threshold T used in the i-th scan i = T / 2 i-1 , i = 1, 2,... As the number of scans increases, the scan threshold gradually decreases until it equals 1. Therefore, the value of T determines the number of scans of the EZW algorithm.

[0052] (2) Conduct the main scan

[0053] The flowchart of the main scan is as Figure 5 shown.

[0054] The scanning order is from high level to low level. For three-level wavelet transform, the scanning order is as follows: LL3,

[0055] HL3, LH3, HH3, HL2, LH2, HH2, HL1, LH1, HH1.

[0056] During the i-th (i = 1, 2,...) scan, the wavelet coefficients are compared with the threshold T i in sequence according to the scanning order. If the absolute value of the wavelet coefficient is greater than or equal to the threshold, it is a significant coefficient; otherwise, it is an insignificant coefficient, and the following output symbols are marked:

[0057] Positive significant coefficient P: The current coefficient is positive and its absolute value is greater than or equal to the threshold;

[0058] Negative significant coefficient N: The current coefficient is negative and its absolute value is greater than or equal to the threshold;

[0059] Zero tree root ZTR: The current coefficient is an insignificant coefficient and all its descendant coefficients are insignificant coefficients;

[0060] Isolated zero IZ: The current coefficient is an insignificant coefficient, but at least one of its descendant coefficients is a significant coefficient.

[0061] Output a secondary table at the same time, and record the important coefficients in the secondary table. At the end of the i-th main scan, set the important coefficients in the wavelet coefficient distribution to zero to avoid encoding them in the next scan.

[0062] (3) Perform a secondary scan

[0063] Scan the secondary table. If the absolute value of the important coefficient is within the interval of [T i , T i +T i / 2], it is encoded as 0. If the absolute value of the important coefficient is within the interval of [T i +T i / 2, 2T i , it is encoded as 1.

[0064] Output the threshold T used in this scan, the obtained marker sequence, and the encoding sequence for use during decompression. i

[0065] Then replace the threshold T i with T i / 2. If T i / 2 is greater than or equal to 1, perform the next scan; otherwise, end. All the scan outputs constitute the encoding result of the EZW algorithm.

[0066] It can be seen that after a three-level wavelet transform of an 8*8 image, the main low-frequency information is concentrated in the upper left corner. The size of the initial threshold T of the EZW algorithm is basically determined by the result of the three-level transform. The larger the T value, the more tables need to be transmitted, and thus the larger the amount of data transmitted. Taking the example of transmitting one more scan table for calculation, even if the upper left corner is scanned as an important coefficient and the others are not scanned, it needs to transmit one important coefficient and three zero trees, and each symbol needs to be represented by 2 bits. Therefore, at least 8 bits more data need to be transmitted.

[0067] The present invention proposes a method to reduce the initial threshold T. By reducing the initial threshold T, the number of scans of the EZW algorithm is reduced, thereby reducing the number of tables that need to be transmitted, further reducing the overall data transmission volume, and improving the image compression effect.

[0068] The following is a detailed description.

[0069] An embodiment of the present invention, as Figure 1 shown, is an image compression method, including:

[0070] Step S100: Perform a K-level wavelet transform on the input image to obtain the first wavelet coefficient distribution;

[0071] Step S200 determines a common coefficient N for the low-frequency region of the first wavelet coefficient distribution. The common coefficient N does not exceed the difference obtained by subtracting the maximum absolute value of other regions from the minimum value of the low-frequency region. The other regions are all non-low-frequency regions in the first wavelet coefficient distribution.

[0072] Step S300 subtracts the common coefficient N from each value in the low-frequency region to obtain an updated low-frequency region; the updated low-frequency region and other regions constitute the second wavelet coefficient distribution.

[0073] Step S400 transmits the common coefficient N.

[0074] Step S500 encodes the second wavelet coefficient distribution using the embedded zero-tree wavelet coding algorithm and transmits the coding result.

[0075] Specifically, the traditional discrete wavelet transform algorithm is used to perform K-level wavelet transform on the input image, and the wavelet coefficients obtained by decomposition are quantized. The quantized wavelet coefficients constitute the first wavelet coefficient distribution. K is a positive integer.

[0076] Taking an 8*8 image as an example, a two-level wavelet transform (i.e., K = 2) is performed, and the first wavelet coefficient distribution obtained is as Figure 3 shown. Among them, LL2 is the low-frequency region of the first wavelet coefficient distribution. This low-frequency region contains 4 points, and the minimum value of this low-frequency region is the minimum value of these 4 points. LH2, HL2, HH2, HL1, LH1, HH1 constitute the other regions. Suppose the values of the 4 points in the low-frequency region are 220, 200, 190, 180 from high to low, and the maximum absolute value of the wavelet coefficients in other regions is 90. Then the minimum value of the low-frequency region = 180, and the common coefficient N can be selected as a positive number not exceeding 90 (= 180 - 90), such as N = 90, or 80, etc.

[0077] Suppose N is taken as 90, then the values of the updated low-frequency region correspond to 130, 110, 100, 90.

[0078] If an EZW scan is performed on the first wavelet coefficient distribution, according to the aforementioned calculation formula, the initial threshold T is 220. Since the values of the low-frequency region are reduced through step S300, when an EZW scan is performed on the second wavelet coefficient distribution, the initial threshold T is reduced to 130.

[0079] The initial threshold T is greatly reduced, reducing the number of scans of the EZW algorithm, thereby reducing the number of tables that need to be transmitted. In order to correctly restore the first wavelet coefficient distribution on the decoding side, in addition to transmitting the output result of the EZW algorithm's scan of the second wavelet coefficient distribution, the transmission of the common coefficient N also needs to be increased. Since more data transmission is reduced, the overall data transmission volume is reduced, improving the image compression effect.

[0080] During decoding, first restore the second wavelet coefficient distribution by the traditional method, and then compensate each value in the low-frequency region of the second wavelet coefficient distribution with the received common coefficient N (i.e., add the common coefficient N), so as to obtain the first wavelet coefficient distribution.

[0081] N can also take other values. For example, when N is taken as 80, the updated values in the low-frequency region are 140, 120, 110, and 100 respectively. Perform EZW scanning on the second wavelet coefficient distribution, then the initial threshold T drops to 140. Compared with 220, the initial threshold T also decreases a lot, but the decrease amount is less than the case when N is 90.

[0082] The reason for limiting that the common coefficient N does not exceed the difference obtained by subtracting the maximum value of the absolute values of other regions from the minimum value of the low-frequency region is to ensure that the subsequent update of the values in the low-frequency region does not affect the zero-tree structure of the wavelet coefficient distribution, that is, the zero-tree structure of the second wavelet coefficient distribution should be consistent with the zero-tree structure of the first wavelet coefficient distribution. Specifically, it is to ensure that the value of the parent node in the wavelet coefficient distribution is not less than the value of the child node. This can ensure that the quality of image compression remains unchanged.

[0083] By subtracting the common coefficient N from each value in the low-frequency region, the size of the wavelet coefficients in the low-frequency region can be reduced without affecting the zero-tree structure, so as to achieve the purpose of reducing the initial scanning threshold T, thereby reducing the amount of data transmission.

[0084] In this embodiment, by determining a common coefficient and subtracting the common coefficient from each value in the low-frequency region, the initial threshold T of scanning is reduced, and then the number of scanning times of the embedded zero-tree wavelet coding algorithm is reduced, and the number of tables to be transmitted is reduced, so as to reduce the overall data transmission amount and improve the lossy compression performance of the image.

[0085] In one embodiment, the common coefficient N is equal to the difference obtained by subtracting the maximum value of the absolute values of other regions from the minimum value of the low-frequency region.

[0086] This can minimize the initial threshold T of EZW scanning without affecting the zero-tree structure.

[0087] In one embodiment, for a 2 L ×2 M input image, M≥L≥3, set K = L - 1.

[0088] For a 2 L ×2 M input image, M≥L≥3, at most L-level wavelet transform can be performed. K can be set equal to L, and then implement the foregoing embodiment scheme. However, by setting K = L - 1 and performing K-level wavelet transform, one wavelet transform can be reduced, further reducing the image compression processing time and improving the image compression efficiency.

[0089] In one embodiment, for a 2 L ×2 M input image, M≥L≥3, set K = L - m, where m>1.

[0090] For a 2 L ×2 M input image, M≥L≥3, at most L-level wavelet transform can be performed. Assuming L is relatively large, at this time, K = L - m can be set to perform K-level wavelet transform, which can reduce m times of wavelet transform, and at the same time reduce the number of levels of embedded zero-tree scanning, speed up the scanning speed, further reduce the image compression processing time, and improve the image compression efficiency.

[0091] This is mainly for relatively large images, which can reduce multiple wavelet transforms. For example, for a 64*64 image, L = M = 6, K can be taken as 4 or 5, which not only improves the image compression efficiency but also ensures the image compression quality. m can be set according to experience.

[0092] An embodiment of the present invention, as Figure 6 shown, an image compression device includes:

[0093] A wavelet transform module 100 for performing K-level wavelet transform on the input image to obtain a first wavelet coefficient distribution;

[0094] A common coefficient determination module 200 for determining a common coefficient N in the low-frequency region of the first wavelet coefficient distribution, where the common coefficient N does not exceed the difference obtained by subtracting the maximum value of the absolute values of other regions from the minimum value of the low-frequency region, and other regions are all non-low-frequency regions in the first wavelet coefficient distribution;

[0095] A low-frequency update module 300 for subtracting the common coefficient N from each value in the low-frequency region to obtain an updated low-frequency region; the updated low-frequency region and other regions form a second wavelet coefficient distribution;

[0096] An encoding and transmission module 400 for transmitting the common coefficient; using the embedded zero-tree wavelet coding algorithm to encode the second wavelet coefficient distribution and transmitting the encoding result.

[0097] In one embodiment, the common coefficient N is equal to the difference obtained by subtracting the maximum value of the absolute values of other regions from the minimum value of the low-frequency region.

[0098] In one embodiment, for a 2 L ×2 M input image, M≥L≥3, set K = L - 1

[0099] In one embodiment, for a 2 L ×2 MThe input image, where M≥L≥3, set K = L - m, and m > 1.

[0100] In one embodiment, during decoding, after restoring the second wavelet coefficient distribution, each value in the low-frequency region of the second wavelet coefficient distribution is compensated correspondingly according to the received common coefficients.

[0101] It should be noted that the embodiments of the image compression device provided by the present invention and the embodiments of the image compression method provided above are all based on the same inventive concept and can achieve the same technical effects. Therefore, other specific contents of the embodiments of the image compression device can refer to the description of the contents of the embodiments of the foregoing image compression method.

[0102] One embodiment of the present invention, as Figure 7 shown, an image compression device includes:

[0103] A memory 10 for storing a computer program 20;

[0104] A processor 30 for implementing the image compression method described in any of the foregoing embodiments when running the computer program 20.

[0105] The memory 10 can be any internal storage unit and / or external storage device capable of realizing data and program storage. For example, the memory 10 can be a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0106] As needed, the processor 10 can be a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a general-purpose processor, or other logic devices, etc.

[0107] One embodiment of the present invention, a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it can implement the image compression method described in the foregoing embodiments. That is, when part or all of the technical solutions that contribute to the prior art in the foregoing embodiments of the present invention are embodied in the form of a computer software product, the foregoing computer software product is stored in a computer-readable storage medium. The computer-readable storage medium can be any device or equipment capable of carrying computer program code entities. For example, a USB flash drive, a removable disk, a magnetic disk, an optical disc, a computer memory, a read-only memory, a random access memory, etc.

[0108] It should be noted that the above embodiments can be freely combined as needed. The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can still be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An image compression method, characterized in that: include: Perform K-level wavelet transform on the input image to obtain the first wavelet coefficient distribution; Determine a common coefficient N of a low-frequency region of the first wavelet coefficient distribution, wherein the common coefficient N does not exceed a difference obtained by subtracting a maximum absolute value of other regions from a minimum value of the low-frequency region, wherein the other regions are all non-low-frequency regions in the first wavelet coefficient distribution; Subtracting the common coefficient N from each value of the low-frequency region to obtain an updated low-frequency region; the updated low-frequency region and the other regions constitute a second wavelet coefficient distribution; transmitting the common coefficients; Encoding the second wavelet coefficient distribution using an embedded zerotree wavelet coding algorithm, and transmitting the coding result; During decoding, after the second wavelet coefficient distribution is restored, each value in the low-frequency region of the second wavelet coefficient distribution is compensated accordingly according to the received common coefficients.

2. The image compression method according to claim 1, characterized in that: The common coefficient N is equal to the difference obtained by subtracting the maximum absolute value of other regions from the minimum value of the low-frequency region.

3. The image compression method according to claim 1, characterized in that: Target 2 L ×2 M For the input image, M≥L≥3, set K=L-1.

4. The image compression method according to claim 1, characterized in that: Target 2 L ×2 M For the input image, M≥L≥3, set K=Lm, m>1.

5. An image compression device, characterized in that: include: A wavelet transform module, used for performing a K-level wavelet transform on the input image to obtain a first wavelet coefficient distribution; A common coefficient determination module, used to determine a common coefficient N of a low-frequency region of the first wavelet coefficient distribution, wherein the common coefficient N does not exceed a difference obtained by subtracting a maximum absolute value of other regions from a minimum value of the low-frequency region, wherein the other regions are all non-low-frequency regions in the first wavelet coefficient distribution; A low frequency updating module, configured to subtract the common coefficient N from each value of the low frequency region to obtain an updated low frequency region; the updated low frequency region and the other regions constitute a second wavelet coefficient distribution; A coding transmission module, used for transmitting the common coefficients; encoding the second wavelet coefficient distribution by using an embedded zerotree wavelet coding algorithm, and transmitting the coding result; During decoding, after the second wavelet coefficient distribution is restored, each value in the low-frequency region of the second wavelet coefficient distribution is compensated accordingly according to the received common coefficients.

6. The image compression device according to claim 5, characterized in that: The common coefficient N is equal to the difference obtained by subtracting the maximum absolute value of other regions from the minimum value of the low-frequency region.

7. The image compression device according to claim 5, characterized in that: Target 2 L ×2 M For the input image, M≥L≥3, set K=L-1, or K=Lm, m>1.

8. An image compression device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the image compression method according to any one of claims 1 to 4 when running the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the image compression method according to any one of claims 1 to 4 is implemented.

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