CN115037937B - Image compression method, apparatus, device and medium - Google Patents

Abstract

The present disclosure provides an image compression method, device, equipment, and medium, wherein the method includes: acquiring an original image to be compressed; acquiring the target category to which the original image belongs from a variety of preset image categories, and the target The compression loss threshold corresponding to the category and a plurality of image compression ratios; the plurality of image compression ratios are sequentially compressed on the original image in order from small to large until the target compressed image is obtained; the target compressed image It is a compressed image whose compression loss is closest to the compression loss threshold and not greater than the compression loss threshold among the compressed images corresponding to the image compression ratio. The disclosure can compress the original image as much as possible on the basis of meeting the image requirements (not greater than the compression loss threshold), and better improve the image compression effect on the basis of saving labor costs.

Description

Image compression method, device, equipment and medium

technical field

The present disclosure relates to the field of image processing, in particular to an image compression method, device, equipment and medium.

Background technique

Image compression is the application of data compression technology on digital images. In many occasions, images need to be compressed. By compressing images, the space required for storing images can be saved or the time required for transmitting images can be shortened. In platforms such as Unity, users usually need to manually set the compression ratio of the image based on experience, and then perform image compression based on the compression ratio set by the user, but this method is prone to problems such as the compressed image is still too large or distorted, so It also requires the user to repeatedly adjust and modify the image compression ratio, which is time-consuming and labor-intensive, and requires high labor costs.

Contents of the invention

In order to solve the above technical problems or at least partly solve the above technical problems, the present disclosure provides an image compression method, device, equipment and medium.

According to one aspect of the present disclosure, there is provided a method of obtaining an original image to be compressed; obtaining the target category to which the original image belongs, as well as the compression loss threshold corresponding to the target category and multiple Image compression ratio; perform compression processing on the original image sequentially in order of a plurality of the image compression ratios from small to large until the target compressed image is obtained; the target compressed image is a plurality of the image compression ratios corresponding to Among the compressed images, the compressed images whose compression loss is closest to the compression loss threshold and not greater than the compression loss threshold.

According to another aspect of the present disclosure, an image compression device is provided, including: an original image acquisition module, used to acquire the original image to be compressed; a threshold and ratio acquisition module, used to acquire the The target category to which the original image belongs, and the compression loss threshold and multiple image compression ratios corresponding to the target category; the image compression module is used to sequentially compare the multiple image compression ratios in order from small to large The original image performs compression processing until the target compressed image is obtained; the target compressed image is a plurality of compressed images corresponding to the image compression ratio, and the compression loss is the closest to the compression loss threshold and is not greater than the compression loss threshold compressed image.

According to another aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory storing a program, wherein the program includes instructions which, when executed by the processor, cause the processor to Execute the image compression method described above.

According to another aspect of the present disclosure, there is provided a computer-readable storage medium storing a computer program for executing the above image compression method.

The above technical solutions provided in the embodiments of the present disclosure obtain the target category to which the original image belongs, as well as the compression loss threshold and multiple image compression ratios corresponding to the target category from various preset image categories, which can be more suitable for the original image. The compression loss threshold and multiple image compression ratios that can be used to compress the original image, and then perform compression processing on the original image in order of multiple image compression ratios in order from small to large, until the compression loss and compression loss threshold are obtained The target compressed image that is closest to and not greater than the compression loss threshold; the above method does not need to manually set the compression ratio based on experience and adjust it repeatedly, but can use multiple image compression ratios suitable for the original image to try to compress from small to large until the target compressed image is obtained , the target compressed image is closest to and not greater than the compression loss threshold, so the above method can compress the original image as much as possible on the basis of meeting the image requirements (not greater than the compression loss threshold), and on the basis of saving labor costs , and also better improve the image compression effect.

It should be understood that what is described in this section is not intended to identify key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will be readily understood through the following description.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description serve to explain the principles of the disclosure.

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, In other words, other drawings can also be obtained from these drawings without paying creative labor.

FIG. 1 is a schematic flowchart of an image compression method provided by an embodiment of the present disclosure;

FIG. 2 is a schematic flowchart of an image compression method provided by an embodiment of the present disclosure;

FIG. 3 is a schematic structural diagram of an image compression device provided by an embodiment of the present disclosure;

Fig. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.

Detailed ways

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the drawings, it should be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein; A more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for exemplary purposes only, and are not intended to limit the protection scope of the present disclosure.

It should be understood that the various steps described in the method implementations of the present disclosure may be executed in different orders, and/or executed in parallel. Additionally, method embodiments may include additional steps and/or omit performing illustrated steps. The scope of the present disclosure is not limited in this respect.

As used in this disclosure, the term "comprise" and its variations are open-ended, ie "including but not limited to". The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one further embodiment"; the term "some embodiments" means "at least some embodiments." Relevant definitions of other terms will be given in the description below. It should be noted that concepts such as "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the sequence of functions performed by these devices, modules or units or interdependence.

It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more" multiple".

In order to more clearly understand the above objects, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, in the case of no conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other.

In platforms such as Unity, users usually need to manually set the image compression ratio based on experience. The compressed image may not meet the requirements (still too large or distorted). If the compressed image is still too large, it is not convenient for image storage or transmission. If the compressed image is distorted, it may seriously affect the subsequent image application. Moreover, in a content development platform such as Unity, each development project may involve a large number of pictures. If each picture needs to be manually compressed by the user, and it is also necessary to manually check whether the compressed image meets the requirements, it is very labor-intensive. . In order to at least partly improve the above problems, embodiments of the present disclosure provide an image compression method, device, device, and medium, which will be described in detail below.

Fig. 1 is a schematic flow chart of an image compression method provided by an embodiment of the present disclosure. The method can be executed by an image compression device, wherein the device can be implemented by software and/or hardware, and can generally be integrated in an electronic device. The electronic device Including but not limited to mobile phones, computers, servers, portable wearable devices, robots and other devices with image processing capabilities, as shown in Figure 1, the method mainly includes the following steps S102 to S106:

Step S102, acquiring the original image to be compressed.

The embodiment of the present disclosure does not limit the content and use of the original image. The original image can be an image selected by the user locally, uploaded by using an external device, or downloaded through the network. The source of the original image is not limited in the embodiment of the present disclosure. Any Raw images that require compression are fine.

In step S104, the target category to which the original image belongs, as well as the compression loss threshold and multiple image compression ratios corresponding to the target category are obtained from various preset image categories.

Wherein, the image compression ratio is a ratio of the size of the original image to the size of the compressed image. The larger the image compression ratio, the smaller the compressed image. In the embodiments of the present disclosure, various image categories may be set in advance based on image content and/or image usage. Exemplarily, multiple image categories can be set based on image content, such as person image categories, landscape image categories, night scene image categories, etc.; multiple image categories can also be set based on image usage, such as image categories for stickers, image categories for Image category as background, image category for target object recognition, etc.; multiple image categories can also be set based on image content and image usage, such as material map category, normal map category, etc. The embodiment of the present disclosure does not limit the manner of classifying image categories.

In some implementations, different image categories correspond to different compression loss thresholds, and/or different image categories correspond to different image compression ratios. That is, according to the characteristics of each image category, corresponding compression loss thresholds and/or various image compression ratios may be set for each image category. For example, most image categories such as person image categories or image categories used for face recognition require high definition, so a relatively small compression loss threshold (that is, an acceptable compression degree) can be set to ensure The compressed image still has a good definition; another example is that the definition requirement for the image category used as the background is usually not high, so a relatively large compression loss threshold can be set (that is, the acceptable compression degree is relatively low). High) to compress such images as much as possible. Similarly, the number of available image compression ratios corresponding to different image categories can be different, such as setting 2 image compression ratios for the person image category, setting 3 image compression ratios for the landscape image category, etc., and the image compression ratios corresponding to different image categories The specific values of can be the same or different, which can be flexibly set according to the actual situation, and are not limited here.

After the original image is obtained, based on the existing multiple image categories, the target category to which the original image belongs can be determined according to the content of the original image or the purpose of the original image, and then the compression loss threshold and multiple image compression ratios corresponding to the target category can be obtained .

Step S106, performing compression processing on the original image sequentially with multiple image compression ratios in ascending order until the target compressed image is obtained; the target compressed image is the compression loss and compression loss in the compressed image corresponding to the multiple image compression ratios Compressed images whose threshold is closest to and not greater than the compression loss threshold.

By compressing multiple image compression ratios in order from small to large, the original image is compressed sequentially, that is, the compression ratio of the compressed image is gradually increased until the target compressed image is obtained, and the compression loss of the target compressed image is closest to the compression loss threshold, and the compression loss of the target compressed image is not greater than the compression loss threshold.

In practical applications, the minimum ratio of multiple image compression ratios is used as the current image compression ratio one by one, and the original image is compressed; if the compression loss of the compressed image corresponding to the current image compression ratio is less than the compression loss threshold, you can Sorting according to the ratio, the next image compression ratio is used to further compress the original image until the final image compression ratio (hereinafter referred to as the termination ratio) is reached. Wherein, the compression loss value of the compressed image obtained by the termination ratio is greater than or equal to the compression loss threshold, or the termination ratio is the largest image compression ratio among multiple image compression ratios (hereinafter referred to as the maximum ratio). Afterwards, the target compressed image may be further obtained according to the comparison result between the compression loss of the compressed image corresponding to the final ratio and the compression loss threshold. For example, when the compression loss of the compressed image corresponding to the termination ratio is equal to the compression loss threshold, the compressed image corresponding to the termination ratio is used as the target compressed image, and when the compression loss of the compressed image corresponding to the termination ratio is greater than the compression loss threshold, the termination The compressed image corresponding to the image compression ratio before the ratio is used as the target compressed image. When the final ratio is the maximum ratio and the compression loss of the compressed image with the maximum ratio is less than the compression loss threshold, the compressed image with the maximum ratio is directly used as the target compressed image.

The above method does not need to manually set the compression ratio based on experience and adjust it repeatedly. Instead, it can use multiple image compression ratios suitable for the original image from small to large and try to compress until the target compressed image is obtained. The target compressed image is closest to and not greater than the compression loss threshold. (determined based on the image category of the original image), so the above method can compress the original image as much as possible on the basis of meeting the image requirements (not greater than the compression loss threshold), and saves labor costs. Improved the image compression effect.

In some implementation examples, the multiple image compression ratios are sequentially compressed on the original image in ascending order until the target compressed image is obtained. Take the target ratio in order from small to large, and use the target ratio to perform compression processing on the original image to obtain the compressed image corresponding to the target ratio; then obtain the compression loss value of the compressed image corresponding to the target ratio; then based on the compression loss value and The comparison result of the compression loss threshold updates the target ratio until the target compressed image is obtained. Through the above method, it is helpful to gradually find the target compressed image that can compress the image to the maximum extent and meet the requirements (not greater than the compression loss threshold).

Specifically, the target ratio is updated based on the comparison result of the compression loss value and the compression loss threshold until the step of obtaining the target compressed image can be based on the specific situation of the target ratio and the compression loss value corresponding to the target ratio as follows (1) ~(4) Execution:

(1) When the target ratio is not the largest ratio among multiple image compression ratios, and the compression loss value corresponding to the target ratio is less than the compression loss threshold, update the target ratio until the largest ratio is used as the target ratio, or until the target ratio The compression loss value corresponding to the ratio is greater than or equal to the compression loss threshold.

Specifically, the next image compression ratio ranked in the target ratio is used as the updated target ratio, and then the original image is compressed using the updated target ratio until the largest ratio is used as the target ratio, or until the target ratio corresponds to The compression loss value of is greater than or equal to the compression loss threshold, then stop updating the target ratio.

(2) When the target ratio is the maximum ratio, and the compression loss value corresponding to the target ratio is less than or equal to the compression loss threshold, the compressed image corresponding to the maximum ratio is used as the target compressed image. That is, if the maximum ratio has been tried, and the original image is compressed with the maximum ratio to obtain a compressed image, and the compression loss value of the compressed image is still not greater than the compression loss threshold, the compressed image corresponding to the maximum ratio is directly used as the target compressed image , at this time to achieve the maximum image compression effect.

(3) When the compression loss value corresponding to the target ratio is greater than the compression loss threshold, the compressed image corresponding to the image compression ratio that is one place ahead of the target ratio is used as the target compressed image. That is to say, the original image is compressed in ascending order of the compression ratios of multiple images until the image compression ratio (that is, the termination ratio) whose compression loss value is greater than the compression loss threshold is tried, the previous image of the termination ratio The compression ratio is the ratio that can compress the original image to the greatest extent, and the compressed image obtained by the previous image compression ratio on the original image meets the requirement that it is closest to the compression loss threshold and not greater than the compression loss threshold.

(4) When the compression loss value corresponding to the target ratio is equal to the compression loss threshold, the compressed image corresponding to the target ratio is used as the target compressed image. When the compression loss value of the target ratio is equal to the compression loss threshold, it means that the compressed image corresponding to the target ratio just meets the requirement that the compression loss threshold is closest to and not greater than the compression loss threshold.

For ease of understanding, for example, the order of multiple image compression ratios from small to large is ratio 1, ratio 2, ratio 3, and ratio N (that is, the largest ratio among the plurality of image compression ratios), then first use Scale 1 performs compression processing on the original image (at this time, Scale 1 is the target scale) to obtain the compressed image corresponding to Scale 1; if the compression loss of the compressed image is less than the compression loss threshold, continue to use Scale 2 to perform compression processing on the original image (At this time, the ratio 2 is used as the target ratio, that is, the target ratio is updated), and the compressed image corresponding to ratio 2 is obtained; if the compression loss of the compressed image corresponding to ratio 2 is less than the compression loss threshold, continue to use ratio 3 to perform on the original image Compress the image (at this time, the ratio 3 is used as the target ratio, that is, the target ratio is updated), and so on, until the target compressed image whose compression loss is closest to the compression loss threshold and not greater than the compression loss threshold is obtained. For example, if the compression loss of the compressed image corresponding to ratio 3 is greater than the compression loss threshold, then use the compressed image corresponding to ratio 2 as the target compressed image that is closest to the compression loss threshold and not greater than the compression loss threshold, or if the compression loss corresponding to ratio 3 If the compression loss of the image is equal to the compression loss threshold, the compressed image corresponding to ratio 3 will be used as the target compressed image; after that, image compression attempts will no longer be performed with ratio 4 to ratio N, because the compression loss of the compressed image corresponding to ratio 4 to ratio N is It is bound to become larger and larger, and both are greater than the compression loss threshold. Of course, if the compressed images corresponding to the ratios before the ratio N are all smaller than the compression loss threshold, then when the compressed image corresponding to the ratio N is less than or equal to the compression loss threshold, the compressed image corresponding to the ratio N will be used as the target compressed image, and at the ratio N When the corresponding compressed image is greater than the compression loss threshold, the compressed image corresponding to the ratio N−1 is used as the target compressed image. Through the above manner, the target compressed image can be efficiently acquired.

The embodiment of the present disclosure provides a specific implementation example of obtaining the compression loss value of the compressed image corresponding to the target ratio, including the following steps A to C:

Step A, based on the pixel values of the original image and the pixel values of the compressed image corresponding to the target ratio, determine the pixel loss value of the compressed image corresponding to the target ratio.

In some implementations, the loss value can be calculated based on each pixel value in the original image and the corresponding pixel value in the compressed image corresponding to the target ratio, and a weighted average is performed on the loss values corresponding to all pixel values to obtain the pixel of the compressed image loss value. The above method can reasonably and objectively evaluate the pixel loss of the compressed image.

Step B: Determine the smoothing loss value of the compressed image corresponding to the target ratio according to the line smoothness of the original image and the line smoothness of the compressed image corresponding to the target ratio.

In some embodiments, the line smoothness is characterized based on the number of jagged lines; and the number of jagged lines is negatively correlated with the smoothness of the line, that is, the greater the number of jagged lines, the lower the smoothness of the line. On this basis, when determining the smoothness loss value of the compressed image corresponding to the target ratio according to the line smoothness of the original image and the line smoothness of the compressed image corresponding to the target ratio, it can first be based on the number of line jaggies in the original image and the target ratio. The number of line aliases in the compressed image corresponding to the ratio, determine the line aliasing increment of the compressed image corresponding to the target ratio; and then determine the target increment interval to which the line aliasing increment of the compressed image belongs according to the preset multiple aliasing increment intervals ;Finally, according to the preset correspondence between each sawtooth increment interval and the smoothing loss value, obtain the smoothing loss value corresponding to the target increment interval, and use the obtained smoothing loss value as the smoothing value corresponding to the line sawtooth increment of the compressed image loss value. In practical applications, the range of the incremental interval can be flexibly set according to requirements, and there is no limitation here. Through the above method, the line smoothness of the image can be estimated conveniently and accurately.

Step C, generating a compression loss value of the compressed image corresponding to the target ratio according to the pixel loss value and the smoothing loss value.

In some specific implementation examples, the first weight corresponding to the pixel loss value and the second weight corresponding to the smoothing loss value may be obtained first, and the pixel loss value and the smoothing loss value are normalized; then according to the first weight and The second weight is to perform a weighted average operation on the normalized pixel loss value and the normalized smoothing loss value, and use the operation result as the compression loss value of the compressed image corresponding to the target ratio. In practical applications, the first weight and the second weight can be flexibly set according to requirements, and the values of the first weight and the second weight can be the same or different, so as to adjust the effect of pixel loss and smoothing loss on the entire compression loss of the compressed image. Influence.

The foregoing method provided by the embodiments of the present disclosure can comprehensively measure the pixel loss and the smoothing loss, so as to objectively evaluate the compression loss of the compressed image, thereby ensuring the accuracy of the compression loss value.

On the basis of the foregoing embodiments, for ease of understanding, you can also refer to the schematic flowchart of the image compression method shown in FIG. 2 , which mainly includes the following steps S202 to S216:

Step S202, acquiring an original image.

In step S204, the target category to which the original image belongs, as well as the compression loss threshold and multiple image compression ratios corresponding to the target category are obtained from various preset image categories.

Step S206 , sorting the multiple image compression ratios in descending order to obtain a sorting result.

Step S208, determining the current target ratio from multiple image compression ratios according to the sorting result. The initial target ratio is the minimum value of multiple image compression ratios, and then the target ratios can be updated sequentially until there is no need to update the target ratio.

Step S210, acquiring the compression loss value of the compressed image corresponding to the target ratio.

Step S212, comparing the compression loss value with the compression loss threshold to obtain a comparison result.

Step S214, judging whether to update the target ratio according to the comparison result, if yes, execute step S208; if not, execute step S216. Specifically, in a case where the target ratio is not the largest ratio among multiple image compression ratios, and the compression loss value corresponding to the target ratio is smaller than the compression loss threshold, it is determined that the target ratio needs to be updated.

Step S216, acquiring the target compressed image according to the comparison result. Specifically, when the target ratio is the maximum ratio, and the compression loss value corresponding to the target ratio is less than or equal to the compression loss threshold, the compressed image corresponding to the maximum ratio is used as the target compressed image; when the compression loss value corresponding to the target ratio is greater than In the case of the compression loss threshold, the compressed image corresponding to the image compression ratio that ranks first in the target ratio is used as the target compressed image; when the compression loss value corresponding to the target ratio is equal to the compression loss threshold, the compression ratio corresponding to the target ratio image as the target compressed image.

Through the above method, there is no need to manually set the compression ratio based on experience and adjust it repeatedly. Instead, multiple image compression ratios suitable for the original image can be used to try to compress from small to large until the target compressed image is obtained. The target compressed image is closest to and not greater than the compressed image. The loss threshold, so the above method can compress the original image as much as possible on the basis of meeting the image requirements (not greater than the compression loss threshold), and there is no need to manually check the compressed image. On the basis of saving labor costs, It also improves the image compression effect better.

It should be noted that the embodiments of the present disclosure do not limit the image compression scenarios. For example, the above-mentioned image compression method provided by the embodiments of the present disclosure can be applied to the Unity content development platform, which can be widely used in games, automobiles, etc. , architectural engineering, film and television animation and other fields, which can be used to create, operate and realize any real-time interactive 2D and 3D content. In the Unity content development platform, it usually involves a large number of images that need to be processed, and usually requires manual processing. For manual compression, it is also necessary to manually check whether the compressed image obtained meets the requirements (such as whether it is distorted, etc.), and the above-mentioned image compression method provided by the embodiment of the present disclosure greatly saves labor costs, improves image compression efficiency, and effectively guarantees The image compression effect is improved, which can better improve the user experience.

Corresponding to the aforementioned image compression method, an embodiment of the present disclosure also provides an image compression device. FIG. 3 is a schematic structural diagram of an image compression device provided by an embodiment of the present disclosure. The device can be implemented by software and/or hardware, and generally can integrated in electronic equipment. As shown in Figure 3, the image compression device 300 includes:

An original image acquisition module 302, configured to acquire an original image to be compressed;

Threshold and ratio acquisition module 304, configured to acquire the target category to which the original image belongs, as well as the compression loss threshold and multiple image compression ratios corresponding to the target category from a variety of preset image categories;

The image compression module 306 is used to compress multiple image compression ratios sequentially on the original image in order from small to large until the target compressed image is obtained; the target compressed image is compressed in the compressed image corresponding to multiple image compression ratios Compressed images whose loss is closest to and not greater than the compression loss threshold.

The above device does not need to manually set the compression ratio based on experience and adjust it repeatedly, but can use multiple image compression ratios suitable for the original image from small to large to try to compress until the target compressed image is obtained, and the target compressed image is closest to and not greater than the compression loss threshold , so the above method can compress the original image as much as possible on the basis of meeting the image requirements (not greater than the compression loss threshold), and can better improve the image compression effect on the basis of saving labor costs.

In some embodiments, the device further includes a category setting module, configured to pre-set multiple image categories based on image content and/or image usage.

In some implementations, different image categories correspond to different compression loss thresholds, and/or different image categories correspond to different image compression ratios.

In some implementations, the image compression module 306 is specifically configured to: take multiple image compression ratios in ascending order as target ratios, and use the target ratios to perform compression processing on the original image to obtain A compressed image corresponding to the target ratio; acquiring a compression loss value of the compressed image corresponding to the target ratio; updating the target ratio based on a comparison result between the compression loss value and the compression loss threshold until the target ratio is obtained Compress images.

In some implementations, the image compression module 306 is specifically used for: when the target ratio is not the largest ratio among multiple image compression ratios, and the compression loss value corresponding to the target ratio is smaller than the compression loss threshold Next, update the target ratio until the maximum ratio is used as the target ratio, or until the compression loss value corresponding to the target ratio is greater than or equal to the compression loss threshold; when the target ratio is the maximum ratio, and the compression loss value corresponding to the target ratio is less than or equal to the compression loss threshold, use the compressed image corresponding to the maximum ratio as the target compressed image; if the compression loss value corresponding to the target ratio is greater than In the case of the compression loss threshold, the compressed image corresponding to the image compression ratio that is one bit ahead of the target ratio is used as the target compressed image; when the compression loss value corresponding to the target ratio is equal to the compression loss threshold Next, use the compressed image corresponding to the target ratio as the target compressed image.

In some implementations, the image compression module 306 is specifically configured to: determine the pixel loss value of the compressed image corresponding to the target ratio based on the pixel value of the original image and the pixel value of the compressed image corresponding to the target ratio; The line smoothness of the original image and the line smoothness of the compressed image corresponding to the target ratio determine the smoothing loss value of the compressed image corresponding to the target ratio; according to the pixel loss value and the smoothing loss value, generate The compression loss value of the compressed image corresponding to the target ratio.

In some implementations, the line smoothness is characterized based on the number of jagged lines; the image compression module 306 is specifically configured to: according to the number of jagged lines in the original image and the number of jagged lines in the compressed image corresponding to the target ratio , determine the line sawtooth increment of the compressed image corresponding to the target ratio; determine the target increment interval to which the line sawtooth increment of the compressed image belongs according to a plurality of preset sawtooth increment intervals; according to each preset sawtooth The corresponding relationship between the incremental interval and the smoothing loss value, obtaining the smoothing loss value corresponding to the target incremental interval, and using the obtained smoothing loss value as the smoothing loss value corresponding to the line sawtooth increment of the compressed image .

In some implementations, the image compression module 306 is specifically configured to: obtain the first weight corresponding to the pixel loss value and the second weight corresponding to the smoothing loss value; Normalization processing; according to the first weight and the second weight, perform a weighted average operation on the normalized pixel loss value and the normalized smoothing loss value, and use the operation result as the The compression loss value of the compressed image corresponding to the above target ratio.

In some embodiments, the image compression method is applied to the Unity content development platform.

The image compression device provided by the embodiments of the present disclosure can execute the image compression method provided by any embodiment of the present disclosure, and has corresponding functional modules and beneficial effects for executing the method.

Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the device embodiment described above can refer to the corresponding process in the method embodiment, and details are not repeated here.

Exemplary embodiments of the present disclosure also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, and when executed by the at least one processor, the computer program is used to cause the electronic device to execute the method according to the embodiment of the present disclosure.

Exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein, when the computer program is executed by a processor of a computer, the computer is used to cause the computer to execute the method.

Exemplary embodiments of the present disclosure also provide a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method according to the embodiments of the present disclosure.

The computer program product can be written in any combination of one or more programming languages to execute the program codes for performing the operations of the embodiments of the present disclosure, and the programming languages include object-oriented programming languages, such as Java, C++, etc. , also includes conventional procedural programming languages, such as the "C" language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server to execute.

In addition, the embodiments of the present disclosure may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the image compression provided by the embodiments of the present disclosure. method. The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

Referring to FIG. 4 , a structural block diagram of an electronic device 400 that can serve as a server or a client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. Electronic device is intended to mean various forms of digital electronic computing equipment, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 4 , the electronic device 400 includes a computing unit 401, which can perform calculations according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. Various appropriate actions and processes are performed. In the RAM 403, various programs and data necessary for the operation of the device 400 can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other through a bus 404. An input/output (I/O) interface 405 is also connected to bus 404 .

Multiple components in the electronic device 400 are connected to the I/O interface 405 , including: an input unit 406 , an output unit 407 , a storage unit 408 and a communication unit 409 . The input unit 406 can be any type of device capable of inputting information to the electronic device 400, and the input unit 406 can receive input digital or character information, and generate key signal input related to user settings and/or function control of the electronic device. The output unit 407 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 408 may include, but is not limited to, a magnetic disk and an optical disk. The communication unit 409 allows the electronic device 400 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks, and may include but not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and/or a chip Groups, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.

The computing unit 401 may be various general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of computing units 401 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 401 executes the various methods and processes described above. For example, in some embodiments, the image compression method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 408 . In some embodiments, part or all of the computer program can be loaded and/or installed on the electronic device 400 via the ROM 402 and/or the communication unit 409 . In some embodiments, the computing unit 401 may be configured to execute the image compression method in any other suitable way (eg, by means of firmware).

Program codes for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special purpose computer, or other programmable data processing devices, so that the program codes, when executed by the processor or controller, make the functions/functions specified in the flow diagrams and/or block diagrams Action is implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or means for providing machine instructions and/or data to a programmable processor (eg, magnetic disk, optical disk, memory, programmable logic device (PLD)), including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with the user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user. ); and a keyboard and pointing device (eg, a mouse or a trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and can be in any form (including Acoustic input, speech input or, tactile input) to receive input from the user.

The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., as a a user computer having a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system can be interconnected by any form or medium of digital data communication, eg, a communication network. Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN) and the Internet.

A computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other.

It should be noted that in this article, relative terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these No such actual relationship or order exists between entities or operations. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

The above descriptions are only specific implementation manners of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (9)

1. An image compression method, comprising: Get the original image to be compressed; Obtaining the target category to which the original image belongs, and the compression loss threshold and multiple image compression ratios corresponding to the target category from various preset image categories; wherein, the compression loss thresholds corresponding to different image categories are different, and /or, multiple image compression ratios corresponding to different image categories are different; Taking a plurality of image compression ratios in order from small to large as target ratios, and using the target ratios to perform compression processing on the original image to obtain a compressed image corresponding to the target ratio; determining a pixel loss value of the compressed image corresponding to the target ratio based on the pixel value of the original image and the pixel value of the compressed image corresponding to the target ratio; determining a smoothing loss value of the compressed image corresponding to the target ratio according to the line smoothness of the original image and the line smoothness of the compressed image corresponding to the target ratio; generating a compression loss value of the compressed image corresponding to the target ratio according to the pixel loss value and the smoothing loss value; The target ratio is updated based on a comparison result between the compression loss value and the compression loss threshold until a target compressed image is obtained; the target compressed image is a compression loss among compressed images corresponding to multiple image compression ratios Compressed images that are closest to the compression loss threshold and not greater than the compression loss threshold. 2. image compression method as claimed in claim 1, wherein, described method also comprises: Various image categories are pre-set based on image content and/or image usage. 3. The image compression method according to claim 1, wherein the target ratio is updated based on a comparison result between the compression loss value and the compression loss threshold, until the step of obtaining the target compressed image comprises: When the target ratio is not the largest ratio among multiple image compression ratios, and the compression loss value corresponding to the target ratio is smaller than the compression loss threshold, update the target ratio until the The maximum ratio is used as the target ratio, or until the compression loss value corresponding to the target ratio is greater than or equal to the compression loss threshold; When the target ratio is the maximum ratio, and the compression loss value corresponding to the target ratio is less than or equal to the compression loss threshold, using the compressed image corresponding to the maximum ratio as the target compressed image; If the compression loss value corresponding to the target ratio is greater than the compression loss threshold, use the compressed image corresponding to the image compression ratio that is one bit ahead of the target ratio as the target compressed image; If the compression loss value corresponding to the target ratio is equal to the compression loss threshold, the compressed image corresponding to the target ratio is used as the target compressed image. 4. The image compression method according to claim 1, wherein the line smoothness is characterized based on the number of jagged lines; according to the line smoothness of the original image and the line smoothness of the compressed image corresponding to the target ratio, The step of determining the smoothing loss value of the compressed image corresponding to the target ratio includes: determining a line jaggy increment of the compressed image corresponding to the target ratio according to the number of line jaggies in the original image and the number of line jaggies in the compressed image corresponding to the target ratio; Determine the target increment interval to which the line sawtooth increment of the compressed image belongs according to a plurality of preset sawtooth increment intervals; According to the preset correspondence between each sawtooth increment interval and smoothing loss value, obtain the smoothing loss value corresponding to the target increment interval, and use the obtained smoothing loss value as the line sawtooth increment of the compressed image. The smoothing loss value corresponding to the amount. 5. The image compression method according to claim 1, wherein, according to the pixel loss value and the smoothing loss value, the step of generating the compression loss value of the compressed image corresponding to the target ratio comprises: Obtaining a first weight corresponding to the pixel loss value and a second weight corresponding to the smoothing loss value; normalizing the pixel loss value and the smoothing loss value; According to the first weight and the second weight, perform a weighted average operation on the normalized pixel loss value and the normalized smoothing loss value, and use the operation result as the target ratio corresponding Compression loss value for compressed images. 6. The image compression method according to any one of claims 1 to 5, wherein the image compression method is applied to the Unity content development platform. 7. An image compression device, comprising: The original image acquisition module is used to obtain the original image to be compressed; A threshold and ratio acquisition module, configured to acquire the target category to which the original image belongs, as well as the compression loss threshold and a plurality of image compression ratios corresponding to the target category from various preset image categories; wherein, different image categories The corresponding compression loss thresholds are different, and/or, the multiple image compression ratios corresponding to different image categories are different; An image compression module, configured to use the multiple image compression ratios in ascending order as target ratios, and use the target ratios to perform compression processing on the original image to obtain a compressed image corresponding to the target ratio ; Based on the pixel value of the original image and the pixel value of the compressed image corresponding to the target ratio, determine the pixel loss value of the compressed image corresponding to the target ratio; according to the line smoothness of the original image and the target ratio Determining a smoothing loss value of the compressed image corresponding to the target ratio corresponding to the line smoothness of the compressed image; generating a compression loss value of the compressed image corresponding to the target ratio according to the pixel loss value and the smoothing loss value; The target ratio is updated based on a comparison result between the compression loss value and the compression loss threshold until a target compressed image is obtained; the target compressed image is a compression loss among compressed images corresponding to multiple image compression ratios Compressed images that are closest to the compression loss threshold and not greater than the compression loss threshold. 8. An electronic device comprising: processor; and memory for storing programs, Wherein, the program includes instructions, and when executed by the processor, the instructions cause the processor to execute the image compression method according to any one of claims 1-6. 9. A computer-readable storage medium, the storage medium stores a computer program, and the computer program is used to execute the image compression method according to any one of claims 1-6.

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