CN112218098A - Data compression method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a data compression method, a data compression device, electronic equipment and a storage medium, wherein the method comprises the steps of obtaining an image to be processed and the fixed deviation of each pixel in the processed image; calculating the variance of the fixed deviation of each pixel in the image to be processed; judging whether the variance of the fixed deviation is lower than a preset threshold value or not; if the variance of the fixed deviation of each pixel in the image to be processed is lower than a preset threshold value, calculating the mean value of the fixed deviation of each pixel in the image to be processed; and compressing the fixed deviation of each pixel in the image to be processed according to the average value of the fixed deviation of each pixel in the image to be processed. The variance of the fixed deviation is calculated for the obtained image and compared with the preset threshold, when the variance is smaller than the threshold, the mean value of the fixed deviation is calculated to compress the fixed deviation, so that the number of the fixed deviations in the image to be stored can be greatly reduced, the storage space and the operation pressure are reduced, and the fixed deviation in the image can be quickly restored.
Description
Technical Field
The present disclosure relates to the field of depth camera technologies, and in particular, to a data compression method and apparatus, an electronic device, and a storage medium.
Background
Time Of Flight (TOF), which transmits continuous infrared light pulses Of a specific wavelength to a target, receives light signals transmitted back by an object to be measured through a specific sensor, calculates the round-trip Flight Time or phase difference Of the light to obtain 3D depth information Of the object to be measured, and presents a three-dimensional profile Of the object in a topographic map mode that different colors represent different distances by combining with a conventional camera for shooting. 3D degree of depth vision TOF module application is extensive, mainly includes: advanced Driver Assistance Systems (ADAS) and Driver Monitoring Systems (DMS), unmanned plane obstacle detection and path planning, industrial robot path planning, and volume measurement in the automotive field.
With the increasing popularization of the TOF technology, the resolution of the TOF module is larger and larger. The resolution is composed of a plurality of pixels in an image, each pixel has a fixed deviation value due to the manufacturing process or the characteristics of the sensor, the fixed deviation of each pixel can be obtained through a calibration process, and the number of the fixed deviations is multiplied along with the increase of the pixels. When the resolution is too large, a large amount of storage space is needed to store the fixed deviation of each pixel, so that the operation cost is increased, and the operation speed is easily reduced due to the overlarge fixed deviation.
Disclosure of Invention
The embodiment of the application provides a data compression method, a data compression device, electronic equipment and a storage medium, which can greatly reduce the number of fixed deviations in an image to be stored, not only slow down the pressure of storage space and operation, but also reduce the cost of storage space investment.
In a first aspect, an embodiment of the present application provides a data compression method, including:
acquiring an image to be processed and the fixed deviation of each pixel in the processed image; the image to be processed is an initial image or a part of the initial image;
calculating the variance of the fixed deviation of each pixel in the image to be processed;
judging whether the variance of the fixed deviation of each pixel is lower than a preset threshold value or not;
if the variance of the fixed deviation of each pixel in the image to be processed is lower than a preset threshold value, calculating the mean value of the fixed deviation of each pixel in the image to be processed;
and compressing the fixed deviation of each pixel in the image to be processed according to the average value of the fixed deviation of each pixel in the image to be processed.
In the embodiment of the application, the variance of the fixed deviation of the acquired image can be calculated and compared with the preset threshold, and when the variance is smaller than the threshold, the mean value of the fixed deviation in the image is calculated to compress the fixed deviation in the image, so that the number of the fixed deviations in the image to be stored can be greatly reduced, the storage space and the operation pressure are relieved, and the cost of the storage space is reduced.
In an alternative of the first aspect, compressing the fixed deviations of the pixels in the image to be processed according to the mean value of the fixed deviations of the pixels in the image to be processed includes:
and replacing the fixed deviation of each pixel in the image to be processed with the mean value of the fixed deviation of each pixel in the corresponding image to be processed.
In the embodiment of the application, the fixed deviation values of all pixels in the image to be processed can be replaced by the fixed deviation average values of all pixels in the image to be processed, and the fixed deviation number of all pixels in the image to be processed is compressed by storing one fixed deviation average value, so that the phenomenon that the occupied running space is too large is avoided.
In yet another alternative of the first aspect, determining whether the variance of the fixed deviation of each pixel is lower than a preset threshold further comprises:
if the variance of the fixed deviation of each pixel in the image to be processed is higher than a preset threshold value, dividing the image to be processed into a plurality of sub-images to be processed, taking the sub-images to be processed as a new image to be processed, and returning the new image to be processed to the step of acquiring the image to be processed and the fixed deviation of each pixel in the processed image.
In the embodiment of the application, as for the variance of the fixed deviation of the pixels in the image to be processed, which is higher than the threshold, it is described that the overall difference of the fixed deviation values of the pixels in the image to be processed is large, the image to be processed needs to be divided, and the divided sub-processed image is continuously used as the image to be processed to calculate the variance and compare the variance with the threshold, so that the effectiveness of data compression can be ensured.
In yet another alternative of the first aspect, dividing the image to be processed into several sub-images to be processed comprises:
dividing an image to be processed into m × n sub-images to be processed;
wherein m is a positive integer, n is a positive integer, and at least one of m and n is greater than or equal to 2.
In the embodiment of the application, when the variance of the fixed deviation of the pixels in the image to be processed is greater than the preset threshold, the image to be processed can be divided into m × n sub-images to be processed, so that the image to be processed is completely divided, and the effectiveness of compressing the fixed deviation quantity is further improved.
In a further alternative of the first aspect, dividing the image to be processed into m × n sub-images to be processed specifically is:
and uniformly dividing the image to be processed into m × n sub-images to be processed according to a preset rule.
In the embodiment of the application, the image to be processed can be uniformly divided into m × n sub-images, so that the size of each sub-image is the same, the compression effect of the fixed deviation number of the pixels in the image to be processed is better, and the compressed fixed deviation can be quickly restored conveniently.
In yet another alternative of the first aspect, replacing the fixed deviation of each pixel in the image to be processed with a mean value of the fixed deviations of each pixel in the image to be processed further comprises:
and saving the position information of the image to be processed on the initial image.
In the embodiment of the application, the mean value of the fixed deviation of the pixels in the image to be processed and the position information corresponding to the whole initial image are saved, so that the fixed deviation of the whole initial image can be quickly restored, the efficiency of data restoration is improved, and then the pixel points corresponding to the fixed deviation of the whole initial image can be conveniently searched.
In yet another alternative of the first aspect, the image to be processed is an initial image;
the acquiring of the image to be processed and the fixed deviation of each pixel in the processed image specifically include:
and carrying out calibration processing on the initial image to obtain the fixed deviation of each pixel in the initial image.
In the embodiment of the application, when the image to be processed is an initial image, the image to be processed may be calibrated to obtain the fixed deviation of each pixel in the image to be processed, so as to facilitate subsequent calculation of the variance and the mean value, thereby implementing compression of the fixed deviation number of the pixels in the image to be processed.
In a second aspect, an embodiment of the present application provides a data compression apparatus, including:
the acquisition module is used for acquiring the image to be processed and the fixed deviation of each pixel in the processed image; the image to be processed is an initial image or a part of the initial image;
the first calculation module is used for calculating the variance of the fixed deviation of each pixel in the image to be processed;
the judging module is used for judging whether the variance of the fixed deviation of each pixel is lower than a preset threshold value or not;
the second calculation module is used for calculating the mean value of the fixed deviation of each pixel in the image to be processed if the variance of the fixed deviation of each pixel in the image to be processed is lower than a preset threshold value;
and the processing module is used for compressing the fixed deviation of each pixel in the image to be processed according to the average value of the fixed deviation of each pixel in the image to be processed.
In the embodiment of the application, the variance of the fixed deviation of the acquired image can be calculated and compared with the preset threshold, and when the variance is smaller than the threshold, the mean value of the fixed deviation in the image is calculated to compress the fixed deviation in the image, so that the number of the fixed deviations in the image to be stored can be greatly reduced, the storage space and the operation pressure are relieved, and the fixed deviation in the image can be quickly restored.
In an alternative of the second aspect, the processing module specifically includes:
and replacing the fixed deviation of each pixel in the image to be processed with the mean value of the fixed deviation of each pixel in the corresponding image to be processed.
In the embodiment of the application, the fixed deviation values of all pixels in the image to be processed can be replaced by the fixed deviation average values of all pixels in the image to be processed, and the fixed deviation number of all pixels in the image to be processed is compressed by storing one fixed deviation average value, so that the phenomenon that the occupied running space is too large is avoided.
In yet another alternative of the second aspect, after the determining module, the method further includes:
and the dividing module is used for dividing the image to be processed into a plurality of sub-images to be processed if the variance of the fixed deviation of each pixel in the image to be processed is higher than a preset threshold value, taking the sub-images to be processed as a new image to be processed, and returning the new image to be processed to the steps of acquiring the image to be processed and the fixed deviation of each pixel in the processed image.
In the embodiment of the application, as for the variance of the fixed deviation of the pixels in the image to be processed, which is higher than the threshold, it is described that the overall difference of the fixed deviation values of the pixels in the image to be processed is large, the image to be processed needs to be divided, and the divided sub-processed image is continuously used as the image to be processed to calculate the variance and compare the variance with the threshold, so that the effectiveness of data compression can be ensured.
In yet another alternative of the second aspect, the dividing module specifically includes:
if the variance of the fixed deviation of each pixel in the image to be processed is higher than a preset threshold value, dividing the image to be processed into m × n sub-images to be processed, taking the sub-images to be processed as new images to be processed, and returning the new images to be processed to the step of obtaining the images to be processed and the fixed deviation of each pixel in the processed images.
In the embodiment of the application, when the variance of the fixed deviation of the pixels in the image to be processed is greater than the preset threshold, the image to be processed can be divided into m × n sub-images to be processed, so that the image to be processed is completely divided, and the effectiveness of compressing the fixed deviation quantity is further improved.
In another alternative of the second aspect, the dividing module further includes:
and if the variance of the fixed deviation of each pixel in the image to be processed is higher than a preset threshold value, uniformly dividing the image to be processed into m × n sub-images to be processed according to a preset rule, taking the sub-images to be processed as a new image to be processed, and returning the new image to be processed to the step of obtaining the image to be processed and the fixed deviation of each pixel in the processed image.
In the embodiment of the application, the image to be processed can be uniformly divided into m × n sub-images, so that the size of each sub-image is the same, the compression effect of the fixed deviation number of the pixels in the image to be processed is better, and the compressed fixed deviation can be quickly restored conveniently.
In yet another alternative of the second aspect, after the processing module, the method further comprises:
and the data storage module is used for storing the position information of the image to be processed on the initial image.
In the embodiment of the application, the mean value of the fixed deviation of the pixels in the image to be processed and the position information corresponding to the whole initial image are saved, so that the fixed deviation of the whole initial image can be quickly restored, the efficiency of data restoration is improved, and then the pixel points corresponding to the fixed deviation of the whole initial image can be conveniently searched.
In yet another alternative of the second aspect, the image to be processed is an initial image;
the acquisition module specifically comprises: the method is used for calibrating the initial image to obtain the fixed deviation of each pixel in the initial image.
In the embodiment of the application, when the image to be processed is an initial image, the image to be processed may be calibrated to obtain the fixed deviation of each pixel in the image to be processed, so as to facilitate subsequent calculation of the variance and the mean value, thereby implementing compression of the fixed deviation number of the pixels in the image to be processed.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, and a communication interface; the processor is connected with the memory and the communication interface; a memory for storing executable program code; the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the data compression method provided by the first aspect of the embodiments of the present application or any implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer storage medium, where a computer program is stored, where the computer program includes program instructions, and when the program instructions are executed by a processor, the data compression method provided by the first aspect of the present application or any implementation manner of the first aspect of the present application may be implemented.
In a fifth aspect, the present application provides a computer program product, which when run on a data compression apparatus, causes the data compression apparatus to execute the data compression method provided in the first aspect of the present application or any implementation manner of the first aspect.
It is to be understood that the electronic device provided by the third aspect, the computer storage medium provided by the fourth aspect, and the computer program product provided by the fifth aspect are all configured to execute the data compression method provided by the first aspect, and therefore, the beneficial effects achieved by the electronic device provided by the third aspect may refer to the beneficial effects in the data compression method provided by the first aspect, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a data compression method according to an embodiment of the present application;
fig. 3 is a schematic diagram illustrating a partitioning manner in a data compression method according to an embodiment of the present application;
fig. 4 is a schematic diagram illustrating a method for storing location information of an image to be processed in a data compression method according to an embodiment of the present application;
fig. 5 is a schematic diagram illustrating another method for compressing data to save location information of an image to be processed according to an embodiment of the present application;
fig. 6 is a schematic flowchart of another data compression method according to an embodiment of the present application;
fig. 7 is a flowchart of a data compression method according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a data compression apparatus according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of another electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
The terms "first," "second," "third," and the like in the description and claims of this application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The device for data compression in the embodiment of the present application may be an electronic device with a depth camera, such as but not limited to a smartphone, a tablet, a camera, a high-speed detection camera, and the like.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
As shown in fig. 1, the electronic device may include one or more depth cameras (i.e., TOF cameras), one or more processors, and one or more memories, wherein the processors may be Mobile Central Processing Units (MCPUs) or Graphics Processing Units (GPUs). Specifically, the depth camera is used for shooting images and sending the obtained images to the processor. The processor is used for receiving the image sent from the depth camera, calculating the variance of the fixed deviation of each pixel in the image and comparing the variance with a preset threshold value stored in the processor. When the variance of the fixed deviation of each pixel in the image is lower than a preset threshold value, the mean value of the fixed deviation of each pixel in the image is continuously calculated, and the fixed deviation of each pixel in the image is compressed according to the mean value of the fixed deviation of each pixel in the image. The further processor sends the fixed deviation of each pixel in the compressed image to the memory for storage.
Illustratively, the image acquired by the depth camera contains a × B pixels. Each pixel corresponds to one fixed deviation, i.e. the image corresponds to a × B fixed deviations. The fixed offset amount of compression by the processor results in a fixed offset amount a b of the compressed image. It should be noted that a is greater than a, B is greater than B, and a × B fixed deviations of the image are sent to the memory for storage by the processor.
The data compression structure provided by the embodiment of the application can be applied to one or more terminals. Possibly, the embodiment of the present application may also be one or more terminals that employ TOF technology. Specifically, TOF techniques can include dToF and iToF techniques. The dToF technology transmits and receives optical signals for N times within a single frame of measurement time, histogram statistics is conducted on the recorded flight times for N times, and the flight time t with the highest occurrence frequency is used for calculating the depth of an object to be measured. The iToF technology is to emit modulated infrared light signals into a scene, receive light signals reflected by an object to be measured in the scene by a sensor, and calculate a phase difference between the emitted signals and the received signals according to accumulated charges within exposure (integration) time, thereby obtaining the depth of the target object.
One or more of the terminals may be hardware or software. In particular, when one or more terminals are hardware, possibly a TOF camera may be installed in the rear camera of one or more terminals, obtaining depth information by the time of the infrared pulse from projection onto the process of return after reflection, mainly applicable to object scanning, indoor navigation, gesture recognition, etc. Among them, one or more terminals may be various electronic devices having a display screen, including but not limited to smart phones, tablet computers, portable computers, and the like.
Possibly, can be provided with TOF module and supporting TOF sensor in one or more terminal, mainly can use TOF technique to realize the advanced driving auxiliary system, driver monitoring system, unmanned aerial vehicle obstacle detection and path planning, industrial robot path planning and volume measurement etc. of automobile field. Among other things, one or more of the terminals may be various electronic devices having a display screen including, but not limited to, a video camera, a high-speed detection camera, and the like.
Specifically, when the one or more terminals are software, the one or more terminals may be installed in the electronic devices listed above, and may be implemented as multiple software or software modules (for example, to provide distributed services), or may be implemented as a single software or software module, which is not limited in this respect.
It should also be noted that the data compression structure can be applied to one or more terminals and servers. Specifically, an image can be obtained through a depth camera arranged at one or more terminals and sent to a server, and a processor arranged in the server completes compression of fixed deviation of each pixel in the image and sends the compressed image to a memory for data storage. The memory can be arranged in the server and also can be arranged in one or more terminals.
The data compression method provided by the embodiment of the present application is described in detail below with reference to fig. 2.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a data compression method according to an embodiment of the present disclosure.
As shown in fig. 2, the data compression method includes:
Specifically, the image to be processed is an initial image or a part of the initial image.
Specifically, when the image to be processed is an initial image, the initial image may be captured by a depth camera (i.e., a TOF camera) of the terminal. Possibly, a user can click photographing software (such as, but not limited to, a camera of the system, a beautiful figure show, and the like) arranged in the terminal to photograph or record a video, and perform camera calibration processing on the acquired image to obtain the fixed deviation of each pixel in the image. The camera calibration method is used for calculating the geometric model parameters of camera imaging for determining the mutual relation between the three-dimensional geometric position of a certain point on the surface of a space object and the corresponding point in an image, and the fixed deviation of image pixels is used as one of the geometric model parameters. Common camera calibration methods include traditional camera calibration methods, active vision camera calibration methods, and camera self-calibration methods.
After the fixed deviation of each pixel in the initial image is obtained, the variance of the fixed deviation of each pixel in the initial image can be obtained by substituting the fixed deviation value of each pixel in the image to be processed into a preset variance calculation formula. The variance is used as the discrete degree of the characterization data, the greater the variance is, the greater the discrete degree of the data is, the greater the integral difference of the data is, the smaller the variance is, the smaller the discrete degree of the data is, and the smaller the integral difference of the data is. Possibly, when the variance of the constant deviation of each pixel in the initial image is small, the overall difference of the constant deviation value of each pixel in the initial image is not large, and the constant deviation value of each pixel in the image is represented by the integrated mean value, so that the compression of the constant deviation amount is realized. Possibly, when the fixed deviation of the pixels in the initial image is large, the difference of the fixed deviation values of the pixels in the initial image is large, the initial image can be processed and divided into a plurality of areas, the variance of the fixed deviation of the pixels in each area is smaller than that of the fixed deviation of the pixels in the initial image, and the compression processing of the fixed deviation quantity is convenient to perform subsequently.
It should be noted that the initial image mentioned in the embodiments of the present application is an initial image in an initial state (without any cropping or smoothing process), the initial image may be composed of one frame of picture, and the resolution of the initial image needs to exceed a preset value. Specifically, for example, the minimum value of the picture resolution may be set to 1024 × 768 in advance, the minimum value is stored in one or more terminals, when the image to be processed is acquired, the resolution of the image to be processed is preferentially checked (specifically, the resolution set by the terminal system may be called up and stored), and if the minimum value is greater than the preset minimum value of the resolution, the variance value of the fixed deviation of each pixel in the image to be processed is calculated. The minimum resolution value of 1024 × 768 can be understood as each horizontal line in the image including 1024 pixels, and there are 768 horizontal lines in total.
Specifically, when the image to be processed is a part of the initial image, indicating that the variance of the fixing deviation of the pixel of the current image to be processed is a variance smaller than the fixing deviation of the pixel of the initial image, it may be further determined whether the fixing deviation of the pixel of the current image to be processed can be subjected to the compression processing by judging the magnitude of the variance. Further, the variance of the fixed deviation of each pixel in the initial image can be obtained by substituting the fixed deviation value of each pixel in the image to be processed into a preset variance calculation formula.
It should be noted that, a part of the initial image is obtained by performing a division process on the initial image, and the part of the initial image may also be composed of one frame of picture, and the resolution of the part of the initial image exceeds a preset value. Specifically, for example, the minimum value of the resolution of the picture may be set to 1024 × 768 in advance, the minimum value is stored in one or more terminals, when the image to be processed is acquired, the resolution of the image to be processed is preferentially determined (specifically, the resolution set by the terminal system may be called up and stored), and if the minimum value of the resolution is greater than the preset minimum value of the resolution, the variance value of the fixed deviation of each pixel in the image to be processed is calculated. The minimum resolution value of 1024 × 768 can be understood as each horizontal line in the image including 1024 pixels, and there are 768 horizontal lines in total.
And step 203, judging whether the variance of the fixed deviation of each pixel is lower than a preset threshold value.
And 204, if the variance of the fixed deviation of each pixel in the image to be processed is lower than a preset threshold value, calculating the mean value of the fixed deviation of each pixel in the image to be processed.
Specifically, the calculated variance of the fixed deviation of each pixel in the current image to be processed is compared with a preset threshold, when the variance of the fixed deviation is lower than or equal to the preset threshold, it is indicated that the dispersion of the fixed deviation of each pixel in the current image to be processed is small, an average value of the fixed deviation of each pixel in the current image to be processed can be calculated, and the value of the fixed deviation of each pixel in the image to be processed is optimized through the average value, so as to facilitate the compression processing of the fixed deviation of each pixel in the image to be processed, and a specific compression manner can be referred to in step 205.
It should be noted that, in the embodiment of the present application, the average value of the fixed deviation of each pixel in the image to be processed may be obtained by substituting the fixed deviation value of each pixel in the image to be processed into a preset average value calculation formula. It is possible to also take the previously calculated average value in the above mentioned preset variance calculation formula as the average value of the fixed deviation of the pixels in the image to be processed.
Specifically, the calculated variance of the fixed deviation of each pixel in the current image to be processed is compared with a preset threshold, and when the variance of the fixed deviation is higher than the preset threshold, it is described that the dispersion of the fixed deviation of each pixel in the current image to be processed is large, and the current image to be processed needs to be optimized.
Possibly, the current image to be processed may be divided into m × n sub-images to be processed, the image to be processed is guaranteed to be completely divided, the variance of the fixed deviation of the pixels in each sub-image to be processed is smaller than the variance of the fixed deviation of the current image to be processed, each sub-image to be processed is sequentially returned to step 201 as a new image to be processed, and the variance of the fixed deviation of each pixel is calculated and compared with a preset threshold. It should be noted that, if the variance of the fixed deviation of each pixel in the sub-image to be processed is greater than the variance of the fixed deviation of each pixel in the current image to be processed, the sub-image to be processed may be continuously divided into m × n sub-images, and the newly divided sub-image may be used as a new image to be processed again.
Specifically, m and n are both positive integers. Both m and n may be positive integers of 2 or more.
Specifically, m and n may both be 2. As shown in fig. 3, the current to-be-processed image is divided into 2 × 2 to-be-processed sub-images, and three types of division modes, non-uniform regular division (as shown in fig. 3 a), uniform regular division (as shown in fig. 3 b), and non-regular division (as shown in fig. 3 c) may occur. And each type of division mode can realize complete division of the current image to be processed, and the sub-images after each type of division mode can be calculated to obtain a variance value of fixed deviation. Preferably, we will find that the uniform regular division shown in fig. 3b uniformly divides the image to be processed into four sub-images to be processed with the same area, so as to accelerate the subsequent calculation speed of the fixed deviation of each pixel in the sub-image to be processed, and the uniform division manner can improve the compression effect and the subsequent restoration effect of the image to be processed.
It should be further noted that, in the embodiment of the present application, the preset threshold may be preset in the terminal or the server, and when the threshold is too large, the fixed deviation of each pixel in the image to be processed may be compressed relatively less, but some details in the image to be processed are easily lost. When the threshold is too small, the fixed deviation of each pixel in the image to be processed is easily compressed, so that an image with a good compression effect can be selected from the database, the variance corresponding to the fixed deviation of each pixel in the image can be calculated, the variance is used as a basic threshold to be compared with the variance of the fixed deviation of each pixel in the image to be processed of the sample, the basic threshold is adjusted by comparing the compression effect of the compressed image and the compressed image of the sample, and finally the threshold with the highest precision is obtained.
And step 205, compressing the fixed deviation of each pixel in the image to be processed according to the average value of the fixed deviations of each pixel in the image to be processed.
Specifically, the fixed deviation value of each pixel in the current image to be processed may be replaced by an average value of the fixed deviations of each pixel in the current image to be processed, the fixed deviation of each pixel in the current image to be processed is deleted, and the average value of the fixed deviation of each pixel in the current image to be processed is stored, thereby implementing compression of the fixed deviation of each pixel in the image to be processed. It should be noted that, image compression itself may damage an initial image, and the method for compressing an image according to the embodiment of the present application may enable a better image compression effect by selecting a preset threshold with high accuracy, and meanwhile, is also convenient for fast restoring a fixed deviation. Specifically, the reduction may be performed by replacing the fixed deviation value of each pixel in the image to be processed with the average value of the fixed deviation of each pixel in the image to be processed, so as to reduce the loss caused by data reduction as much as possible.
Specifically, in order to better and accurately identify the average value of the fixed deviation of each pixel in the saved image to be processed, the position information of the average value in the whole initial image can be further saved.
Possibly, referring to fig. 4, fig. 4 shows a schematic diagram of a user saving position information of an image to be processed. Taking the resolution of the initial image as b × c as an example, a rectangular coordinate system may be established with reference to a lower left vertex of four vertices of the initial image as an origin, and the length and width of the initial image adjacent to the lower left vertex are respectively overlapped with an x axis and a y axis in the rectangular coordinate system, where an upper right vertex may be represented as (b, c).
When the variance of the fixed deviations of the respective pixels in the initial image is lower than a preset threshold, the average of the fixed deviations of the respective pixels in the initial image is calculated, and the center point (0.5b, 0.5c) of the initial image may be selected as the position information to be saved.
When the variance of the fixed deviation of each pixel in the initial image is higher than the preset threshold, preferably, the initial image may be uniformly divided into m × n sub-images to be processed according to a preset rule, where m and n may be 2, and the preset rule may be that midpoints of length and width in the initial image are selected as dividing points, and the midpoints of opposite length and width are respectively connected to uniformly divide the initial image into 4 equal sub-images, which may be respectively represented by a1, a2, A3 and a4 (where the initial image may be represented by a).
Taking the sub-images a1, a2, A3 and a4 as the images to be processed, respectively, the variance of the fixed deviation of each pixel in each sub-image is continuously calculated. Illustratively, taking the case that the variance of the fixed deviations of the pixels in the sub-image a4 is higher than the preset threshold, and the variance of the fixed deviations of the pixels in the sub-images a1, a2 and A3 is lower than the preset threshold, the average of the fixed deviations of the pixels in the sub-images a1, a2 and A3 is calculated and stored, and the coordinates of the center points of the sub-images a1, a2 and A3 are respectively used as the position information to be stored, for example, the position information to be stored in the sub-image a1 is (0.25b, 0.25c), the position information to be stored in the sub-image a2 is (0.75b, 0.25c), and the position information to be stored in the sub-image A3 is (0.25b, 0.75 c). Then, the sub-image a4 is divided into 4 sub-images with the same area uniformly according to the preset rule set before (which can be represented by a41, a42, a43 and a 44), and the variance of the fixed deviation of each pixel in each image is calculated by taking a41, a42, a43 and a44 as new images to be processed. If the variance of the fixed deviations of the pixels in the images a41, a42, a43, and a44 is lower than the preset threshold, the average of the fixed deviations of the pixels in the images a41, the coordinates of the center point of the images a42, the position information of the image a43, and the position information of the image a44 are calculated and stored, respectively, as the position information, for example, (0.625b, 0.625c), (0.875b, 0.875c), and (0.875b, 0.875 c). If the variance of the pixel constant deviations in the images a41, a42, a43, and a44 is higher than the preset threshold, the images may continue to be processed according to the above-mentioned division method, which is not described herein again.
Possibly, the initial image is uniformly divided into m × n sub-images to be processed according to a preset rule, where in the case that m and n are both 2, reference may also be made to another schematic diagram of storing location information provided in the embodiment of the present application shown in fig. 5. As shown in fig. 5, the position information of the image to be processed may be represented by a binary method. Taking an image to be processed as an initial image as an example, specifically, when the variance of the fixed deviation of each pixel in the initial image is higher than a preset threshold, the initial image is uniformly divided into 4 sub-images with the same area, the position information can be respectively represented as (00), (01), (10) and (11), and the variances of the fixed deviations of each pixel in the 4 sub-images are sequentially calculated. If the variance of the constant deviations of the pixels in the sub-image represented by (10) is higher than the preset threshold, and the variances of the constant deviations of the pixels in the sub-image represented by (00), (01), and (11) are lower than the preset threshold, for example, the position information of the sub-image represented by (00), (01), and (11) is saved, and then the sub-image represented by (10) is divided into 4 sub-images with the same area, and the corresponding position information can be represented as (10,00), (10,01), (10,10), and (10,11), respectively. And then taking the new sub-images as new images to be processed respectively to calculate the variance of the fixed deviation of each pixel in the images. If the variance of the fixed deviation of each pixel in the new image to be processed is higher than the preset threshold, the image may be processed continuously according to the above-mentioned dividing method, and the specific content of the location information provided by the above-mentioned information storage method may be referred to, which is not described herein again.
The embodiment of the application provides a data compression method, which can calculate the variance of the fixed deviation of the acquired image and compare the variance with a preset threshold value, and when the variance is smaller than the threshold value, calculate the mean value of the fixed deviation in the image to compress the fixed deviation in the image, so that the number of the fixed deviations in the image to be stored can be greatly reduced, the pressure of a storage space and the operation is relieved, and the cost of the storage space is reduced.
Referring to fig. 6, fig. 6 is a schematic flowchart illustrating another data compression method according to an embodiment of the present application.
As shown in fig. 6, the data compression method includes:
Specifically, the image to be processed is an initial image or a part of the initial image.
Specifically, steps 601 and 602 are identical to steps 201 and 202, respectively, and are not described herein again.
Specifically, when the variance of the fixed deviation is higher than the preset threshold, it indicates that the fixed deviation dispersion of each pixel in the current image to be processed is large, and it is necessary to divide the region so that the fixed deviation dispersion of each pixel in each region image is small, thereby facilitating the compression processing.
Specifically, the manner of dividing the image to be processed into a plurality of sub-images to be processed may be non-uniform regular division, and non-regular division. Similarly, referring to fig. 3, it is preferably found that uniformly dividing the image to be processed into a plurality of sub-images to be processed with the same area facilitates speeding up the subsequent calculation of the fixed deviation of each pixel in the sub-image to be processed, and the uniform division can improve the compression effect and the subsequent restoration effect of the image to be processed.
When the image to be processed is uniformly divided into a plurality of sub-images to be processed with the same area, each sub-image to be processed can be sequentially used as a new image to be processed, and the variance of the fixed deviation of each pixel in the new image to be processed is calculated.
Specifically, step 605 is identical to step 204, and is not described herein again.
And 606, replacing the fixed deviation of each pixel in the image to be processed with the mean value of the fixed deviation of each pixel in the corresponding image to be processed.
Specifically, the fixed deviation of each pixel in the image to be processed may be compressed by replacing the fixed deviation of each pixel in the image to be processed with an average value of the fixed deviations of each pixel in the image to be processed, deleting the fixed deviation of each pixel in the current image to be processed, and storing the average value of the fixed deviations of each pixel in the current image to be processed, thereby compressing the fixed deviation of each pixel in the image to be processed. It should be noted that, image compression itself may damage an initial image, and the method for compressing an image according to the embodiment of the present application may enable a better image compression effect by selecting a preset threshold with high accuracy, and meanwhile, is also convenient for fast restoring a fixed deviation. Specifically, the reduction may be performed by replacing the fixed deviation value of each pixel in the image to be processed with the average value of the fixed deviation of each pixel in the image to be processed, so as to reduce the loss caused by data reduction as much as possible.
Referring to fig. 7, fig. 7 is a flowchart illustrating a data compression method according to an embodiment of the present application.
As shown in fig. 7, taking m and n as 2, the data compression method includes:
And step 704, when the variance of the fixed deviation of the pixels in the image to be processed is lower than a preset threshold value, calculating the average value of the fixed deviation of the pixels in the image to be processed.
Specifically, an initial image to be compressed can be acquired through the depth camera, and the initial image is calibrated to obtain a fixed deviation value of each pixel in the initial image. And then taking the processed initial image as an image to be processed, calculating the variance of the fixed deviation of each pixel, and comparing the variance with a preset threshold value. When the variance is detected to be lower than a preset threshold value, the integral dispersion of the image is small, the average value of the fixed deviation of each pixel can be calculated, the fixed deviation of each pixel in the image is replaced by the average value to realize data compression of the initial image, and finally the average value and the corresponding position information are stored.
When the variance is detected to be lower than the preset threshold, the fact that the dispersion of the fixed deviation of each pixel in the initial image is large is indicated, in order to present a better compression effect, the initial image can be uniformly divided into 4 sub-images to be processed with the same area, each sub-image to be processed is sequentially used as a new image to be processed, the variance is calculated and compared with the preset threshold until the variance of the fixed deviation of each pixel in the final image to be processed is lower than the preset threshold, and finally, the average value of the fixed deviation of each pixel of the image to be processed, which meets the condition that the variance of each pixel is lower than the preset threshold, and the corresponding position information are stored.
It should be noted that, in the embodiment of the present application, the to-be-processed image is not limited to be uniformly divided into 2 × 2 to-be-processed sub-images, and may be m × n, where m and n are at least one positive integer greater than or equal to 2.
Referring to fig. 8, fig. 8 is a schematic structural diagram illustrating a data compression apparatus according to an embodiment of the present disclosure.
As shown in fig. 8, the data compression apparatus 800 includes an obtaining module 801, a first calculating module 802, a determining module 803, a second calculating module 804 and a processing module 805, wherein the details of each module are as follows:
an obtaining module 801, configured to obtain an image to be processed and a fixed deviation of each pixel in the processed image; the image to be processed is an initial image or a part of the initial image;
a first calculating module 802, configured to calculate a variance of a fixed deviation of each pixel in the image to be processed;
a determining module 803, configured to determine whether a variance of the fixed deviation of each pixel is lower than a preset threshold;
a second calculating module 804, configured to calculate a mean value of the fixed deviations of the pixels in the image to be processed if the variance of the fixed deviations of the pixels in the image to be processed is lower than a preset threshold;
the processing module 805 is configured to perform compression processing on the fixed deviations of the pixels in the image to be processed according to the average value of the fixed deviations of the pixels in the image to be processed.
In the embodiment of the application, the variance of the fixed deviation of the acquired image can be calculated and compared with the preset threshold, and when the variance is smaller than the threshold, the mean value of the fixed deviation of each pixel in the image is calculated to compress the fixed deviation in the image, so that the number of the fixed deviations in the image to be stored can be greatly reduced, the storage space and the operation pressure are relieved, and the cost of the storage space is reduced.
As an optional implementation manner, the processing module specifically includes:
and replacing the fixed deviation of each pixel in the image to be processed with the mean value of the fixed deviation of each pixel in the corresponding image to be processed.
As an optional implementation manner, after the determining module, the method further includes:
and the dividing module is used for dividing the image to be processed into a plurality of sub-images to be processed if the variance of the fixed deviation of each pixel in the image to be processed is higher than a preset threshold value, taking the sub-images to be processed as a new image to be processed, and returning the new image to be processed to the steps of acquiring the image to be processed and the fixed deviation of each pixel in the processed image.
As an optional implementation manner, the dividing module specifically further includes:
if the variance of the fixed deviation of each pixel in the image to be processed is higher than a preset threshold value, dividing the image to be processed into m × n sub-images to be processed, taking the sub-images to be processed as new images to be processed, and returning the new images to be processed to the step of obtaining the images to be processed and the fixed deviation of each pixel in the processed images.
As an optional implementation manner, the dividing module specifically further includes:
and if the variance of the fixed deviation of each pixel in the image to be processed is higher than a preset threshold value, uniformly dividing the image to be processed into m × n sub-images to be processed according to a preset rule, taking the sub-images to be processed as a new image to be processed, and returning the new image to be processed to the step of obtaining the image to be processed and the fixed deviation of each pixel in the processed image.
As an optional implementation manner, after the processing module, the method further includes:
and the data storage module is used for storing the position information of the image to be processed on the initial image.
As an optional implementation, the image to be processed is the initial image;
the acquisition module specifically comprises: and the method is used for carrying out calibration processing on the initial image to obtain the fixed deviation of each pixel in the initial image.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
The electronic device 900 may include: at least one processor 901, such as a CPU, at least one network interface 905, a user interface 904, memory 902, at least one communication bus 903, a display screen 906 and at least one depth camera 907 (i.e., a TOF camera). Wherein a communication bus 903 is used to enable connective communication between these components. The user interface 904 may include, but is not limited to, a touch screen, a keyboard, a mouse, a joystick, and the like. Optionally, the network interface 905 may include a standard wired interface or a standard wireless interface (e.g., a WIFI interface or a bluetooth interface), and the network interface 905 may establish a communication connection with the server. The memory 902 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The depth camera 907 is used for acquiring an initial image, and may be disposed at a rear camera of the electronic device. As shown in fig. 9, the memory 902, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.
It should be noted that the network interface 905 may be connected to an acquirer, a transmitter, or another communication module, and the other communication module may include, but is not limited to, a WiFi module, a bluetooth module, and the like, and it is understood that the electronic device 900 in this embodiment may also include an acquirer, a transmitter, and another communication module, and the like.
The processor 901 may be used to call program instructions stored in the memory 902, and may perform the methods provided by the embodiments shown in fig. 2 or fig. 6.
Embodiments of the present application also provide a computer-readable storage medium having stored therein instructions, which when executed on a computer or processor, cause the computer or processor to perform one or more steps of any one of the methods described above. The respective constituent modules of the electronic device described above may be stored in the computer-readable storage medium if they are implemented in the form of software functional units and sold or used as independent products.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in or transmitted over a computer-readable storage medium. The computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. And the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks. The technical features in the present examples and embodiments may be arbitrarily combined without conflict.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. A method of data compression, comprising:
s1, acquiring an image to be processed and the fixed deviation of each pixel in the image to be processed; the image to be processed is an initial image or a part of the initial image;
s2, calculating the variance of the fixed deviation of each pixel in the image to be processed;
s3, judging whether the variance of the fixed deviation of each pixel is lower than a preset threshold value or not;
s4, if the variance of the fixed deviation of each pixel in the image to be processed is lower than a preset threshold value, calculating the mean value of the fixed deviation of each pixel in the image to be processed;
s5, compressing the fixed deviation of each pixel in the image to be processed according to the mean value of the fixed deviation of each pixel in the image to be processed.
2. The method according to claim 1, wherein the compressing the fixed deviation of each pixel in the image to be processed according to the mean of the fixed deviations of each pixel in the image to be processed comprises:
and replacing the fixed deviation of each pixel in the image to be processed with a mean value corresponding to the fixed deviation of each pixel in the image to be processed.
3. The method of claim 1, wherein determining whether the variance of the fixed deviation of each pixel is below a preset threshold further comprises:
if the variance of the fixed deviation of each pixel in the image to be processed is higher than or equal to a preset threshold, dividing the image to be processed into a plurality of sub-images to be processed, taking the sub-images to be processed as a new image to be processed, and returning the new image to be processed to step S1.
4. The method according to claim 3, wherein the dividing the image to be processed into a plurality of sub-images to be processed comprises:
dividing the image to be processed into m × n sub-images to be processed;
wherein m is a positive integer, n is a positive integer, and at least one of m and n is greater than or equal to 2.
5. The method according to claim 4, wherein the dividing of the image to be processed into m x n sub-images to be processed is specifically:
and uniformly dividing the image to be processed into m × n sub-images to be processed according to a preset rule.
6. The method according to claim 2, wherein replacing the fixed deviation of each pixel in the image to be processed with the mean value of the fixed deviation of each pixel in the image to be processed further comprises:
and saving the position information of the image to be processed on the initial image.
7. The method according to claim 6, wherein the image to be processed is the initial image;
the acquiring of the image to be processed and the fixed deviation of each pixel in the processed image specifically include:
and carrying out calibration processing on the initial image to obtain the fixed deviation of each pixel in the initial image.
8. A data compression apparatus, comprising:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an image to be processed and the fixed deviation of each pixel in the processed image; the image to be processed is an initial image or a part of the initial image;
the first calculation module is used for calculating the variance of the fixed deviation of each pixel in the image to be processed;
the judging module is used for judging whether the variance of the fixed deviation of each pixel is lower than a preset threshold value or not;
the second calculation module is used for calculating the mean value of the fixed deviation of each pixel in the image to be processed if the variance of the fixed deviation of each pixel in the image to be processed is lower than a preset threshold value;
and the processing module is used for compressing the fixed deviation of each pixel in the image to be processed according to the average value of the fixed deviation of each pixel in the image to be processed.
9. An electronic device comprising a processor, a memory, and a communication interface;
the processor is connected with the memory and the communication interface;
the memory for storing executable program code;
the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory for performing the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Address after: 330096 No.699 Tianxiang North Avenue, Nanchang hi tech Industrial Development Zone, Nanchang City, Jiangxi Province Applicant after: Jiangxi OMS Microelectronics Co.,Ltd. Address before: 330096 Jiangxi Nanchang Nanchang hi tech Industrial Development Zone, east of six road, south of Tianxiang Avenue. Applicant before: OFilm Microelectronics Technology Co.,Ltd. |
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Effective date of abandoning: 20231117 |
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