CN109672818B - A method and device for adjusting image quality - Google Patents

Abstract

The present application discloses a method and a device for adjusting image quality. The method includes: performing sampling processing on n images continuously obtained in a camera preview process to obtain n sampled images, and according to each adjacent two images in the n sampled images A set of motion areas is determined from the sampled images, and n-1 groups of motion areas are obtained; according to the n-1 groups of motion areas, the pixel displacements corresponding to the n-1 groups of motion areas are determined respectively; the pixel displacements corresponding to the n-1 groups of motion areas are respectively determined Determine the motion information corresponding to the n-1 groups of motion areas respectively. Through the above method, it is possible to reduce the image resolution through sampling processing, obtain a sampled image, and determine motion information according to the sampled image, so that the obtained motion information is used to adjust the image quality. This method has the characteristics of low computational complexity and low power consumption.

Description

A method and device for adjusting image quality

technical field

The present application relates to the fields of image processing and computer vision, and in particular, to a method and apparatus for adjusting image quality.

Background technique

Some mobile electronic devices (eg, mobile phones) are equipped with photographic cameras, and the photographed scene may contain moving objects, and the moving smear of the objects during exposure time will appear blurred, resulting in a low filming rate and affecting the photographing effect. Therefore, it is very important to accurately detect object motion, and exposure parameters can be controlled based on accurate object motion information, thereby improving imaging quality and film yield.

In the prior art, general motion detection schemes are divided into two categories: motion detection schemes based on image processing and motion detection schemes based on external microelectronic devices.

For example, motion detection schemes based on image processing include background methods, optical flow methods, temporal difference methods, and the like. Among them, the background method is highly dependent on the scene and is not suitable for frequently changing scenes. The optical flow method is complicated to calculate, and the time consumption and power consumption are relatively high. The time difference method is susceptible to noise.

The motion detection scheme based on external microelectronic devices relies on the external electronic device to sense the overall displacement of the device, and cannot determine the motion state of the photographed object when the device is stable. Therefore, this type of motion detection scheme is suitable for shooting anti-shake processing. .

To sum up, none of the existing motion detection solutions are suitable for use in photographing moving objects with photographic cameras in mobile electronic devices.

SUMMARY OF THE INVENTION

The present application provides a method and apparatus for adjusting image quality, which are used to provide accurate object motion information for a photographic camera in a mobile electronic device.

In a first aspect, the present application provides a method for adjusting image quality, the method comprising:

The processor performs sampling processing on the n images continuously obtained in the camera preview process to obtain n sampled images, wherein the ith image corresponds to the ith sampled image, and the ith image is one of the n images. For any image of , i and n are positive integers; a group of motion regions is determined according to every two adjacent sampled images in the n sampled images, and n-1 groups of motion regions are obtained, wherein the i-th group of motion regions It is obtained according to the i-th sampled image and the i+1-th sampled image, and the i-th group of motion regions includes the motion region corresponding to the i-th sampled image and the i+1-th sampled image. according to the n-1 groups of motion areas, determine the pixel displacements corresponding to the n-1 groups of motion areas, wherein the pixel displacement corresponding to the i-th group of motion areas refers to the i+1-th group of motion areas The pixel displacement of the motion area corresponding to the sampled images relative to the motion area corresponding to the i-th sampled image; the motion corresponding to the n-1 groups of motion areas is determined according to the pixel displacements corresponding to the n-1 groups of motion areas respectively information, the motion information corresponding to the n-1 groups of motion regions is used to adjust the image quality.

Through the above method, the processor can obtain n sampled images with lower image resolution by sampling the n images, and determine a set of motion regions according to every two adjacent sampled images in the n sampled images, and further calculate The pixel displacements corresponding to the n-1 groups of motion regions, and the motion information corresponding to the n-1 groups of motion regions, respectively, are obtained. Therefore, through the above method, it is possible to reduce the image resolution through sampling processing, obtain a sampled image, and determine motion information according to the sampled image, so that the obtained motion information is used to adjust the image quality. This method has the characteristics of low computational complexity and low power consumption.

In a possible design, the i-th sampled image refers to performing downsampling after a preset filtering process in the horizontal direction of the i-th image, and in the vertical direction of the i-th image Image obtained after performing downsampling.

Therefore, by the above method, the calculation amount and power consumption of the processor can be reduced, and the problem of flickering of the sampled image can be effectively avoided.

In a possible design, the preset filtering process is an infinite impulse response filter IIR filter, or a finite impulse response filter FIR filter.

Therefore, the present application provides multiple possible preset filtering processing methods.

In a possible design, the processor may adopt the following method when determining a group of motion regions according to every two adjacent sampled images in the n sampled images:

For the i-th sampled image and the i+1-th sampled image, perform: dividing both the i-th sampled image and the i+1-th sampled image into M×N rectangular windows, M and N are positive integers; calculate M×N first correlation coefficients, where the kth first correlation coefficient is the kth rectangular window in the i-th sampled image and the i+1-th sampled image The correlation coefficient of the kth rectangular window in , the kth first correlation coefficient is any one of the M×N first correlation coefficients, and k is a positive integer; according to the M×N Among the first correlation coefficients, the rectangular window corresponding to the first correlation coefficient that is less than or equal to the first preset correlation coefficient threshold is determined, and the ith group of motion regions is determined, wherein the motion region corresponding to the ith sampled image includes a rectangle The windows are in one-to-one correspondence with the rectangular windows included in the motion region corresponding to the i+1 th sampled image in the ith group of motion regions.

Therefore, through the above method, the processor can determine that there is a moving object in the i+1 th sampled image relative to the ith sampled image, and determine the region where the moving object is located.

In a possible design, the k-th first correlation coefficient is the projection histogram of the k-th rectangular window in the i-th sampled image in the horizontal direction and the i+1-th sampled image The correlation coefficient of the projection histogram of the kth rectangular window in the horizontal direction and the projection histogram of the kth rectangular window in the vertical direction in the i-th sampling image and the i+1-th sampling image The sum of the correlation coefficients of the projected histograms of the kth rectangular window in the vertical direction.

Therefore, the correlation coefficient algorithm for calculating the first correlation coefficient here may be a projection histogram method, an image histogram method, a color feature method, etc., which is not limited in this application. In order to reduce the computational complexity and power consumption of the processor, the processor can use the projection histogram method to calculate the correlation coefficient of the two rectangular windows.

In a possible design, the processor may use the following method when determining the pixel displacements corresponding to the n-1 groups of motion regions according to the n-1 groups of motion regions:

For the i-th group of motion regions, the motion region corresponding to the i-th sampled image includes m rectangular windows, and the motion region corresponding to the i-th sampled image includes m rectangular windows. Perform: adopting a preset algorithm to calculate m pixel displacements, where the jth pixel displacement is relative to the rectangular window matching the jth rectangular window in the motion region corresponding to the i+1th sampling image in the motion region corresponding to the i+1th sampling image The pixel displacement of the j-th rectangular window in the motion region corresponding to the i-th sampled image, where m and j are both positive integers; the pixel displacement corresponding to the i-th group of motion regions is determined according to the m pixel displacements.

Therefore, through the above method, the processor can realize the determination of the speed and direction of the movement of the object on the basis of lower calculation amount and power consumption.

In a possible design, the motion information corresponding to the ith group of motion areas includes at least one of the following: a pixel displacement corresponding to the ith group of motion areas, a motion speed identifier corresponding to the ith group of motion areas, The motion direction identification corresponding to the i-th group of motion regions; wherein, the motion speed identification corresponding to the i-th group of motion regions is a preset displacement size threshold that falls according to the size of the pixel displacement corresponding to the i-th group of motion regions The motion speed identifier corresponding to the range; the motion direction identifier corresponding to the i-th group of motion areas is the motion direction identifier corresponding to the preset angle range within which the direction of the pixel displacement corresponding to the i-th group of motion areas falls.

Therefore, through the above method, the processor can output various identifications indicating the speed and direction of the movement of the object.

In a possible design, if the m pixel displacements satisfy at least one of the following first judgment criterion and second judgment criterion, the motion information corresponding to the i-th group of motion regions includes a first identifier, and the i-th group of motion regions An identifier is used to indicate that the i-th group of motion regions is in a high-speed motion state; the first judgment criterion means that the number of m second correlation coefficients less than or equal to the second preset correlation coefficient threshold is greater than or equal to the first preset number Wherein, the j-th second correlation coefficient is the rectangular window that matches the j-th rectangular window in the motion region corresponding to the i-th sampled image and the j-th rectangular window in the motion region corresponding to the i-th sampled image and the The correlation coefficient of the jth rectangular window in the motion region corresponding to the ith sampled image. The second judgment criterion refers to that the number of different directions in the displacement directions of the m pixels is greater than or equal to a second preset number.

Therefore, the above method can make up for the deficiency of the calculation error of the SAD scanning algorithm in the fast motion scene, and ensure the accuracy of the object motion information.

In a possible design, before determining the motion information corresponding to the n-1 groups of motion regions, the processor performs low-pass filtering on the pixel displacements corresponding to the n-1 groups of motion regions.

Therefore, the influence of random noise and singular value on the calculation result can be eliminated by the above method, and the stability of the output result can be ensured.

In a possible design, after determining the motion information corresponding to the i-th group of motion regions, the processor may determine if the absolute value of the difference between the size of the pixel displacement corresponding to the i-th group of motion regions and the first threshold is less than or equal to Fluctuation threshold, adjust the first threshold.

Therefore, the above-mentioned method can eliminate the problem of frequent jittering of the motion speed markers that occurs when the magnitude of the displacement of multiple pixels is near the threshold, and ensure the stability of the output result.

In a second aspect, the present application provides an apparatus for adjusting image quality, the apparatus comprising:

The sampling unit is configured to perform sampling processing on the n images continuously obtained in the camera preview process to obtain n sampled images, wherein the ith image corresponds to the ith sampled image, and the ith image is the n For any image in the images, i and n are positive integers;

A processing unit, configured to determine a group of motion regions according to every two adjacent sampled images in the n sampled images, and obtain n-1 groups of motion regions, wherein the i-th group of motion regions is based on the i-th sampled image Obtained from the i+1 th sampled image, the i-th group of motion regions includes a motion region corresponding to the i-th sampled image and a motion region corresponding to the i+1-th sampled image;

a calculation unit, configured to determine the pixel displacements corresponding to the n-1 groups of motion regions according to the n-1 groups of motion regions, wherein the pixel displacement corresponding to the i-th group of motion regions refers to the i-th group of motion regions The pixel displacement of the motion region corresponding to one sampled image relative to the motion region corresponding to the i-th sampled image;

An analysis unit, configured to determine the motion information corresponding to the n-1 groups of motion areas according to the pixel displacements corresponding to the n-1 groups of motion areas, and the motion information corresponding to the n-1 groups of motion areas is used for adjustment Image Quality.

Optionally, the apparatus for adjusting image quality may also implement some or all of the optional implementation manners of the first aspect.

In a third aspect, the present application provides a device for adjusting image quality, the device for adjusting image quality comprising: a memory for storing computer executable program codes; a communication interface, and a processor, where the processor is coupled to the memory and the communication interface. The program code stored in the memory includes instructions, and when the processor executes the instructions, the device for adjusting image quality is made to execute the method of the first aspect.

In a fourth aspect, the present application provides a computer program product, the computer program product comprising: computer program code, when the computer program code is run on a computer, the computer is made to execute any possible implementation manner in the first aspect above method in .

In a fifth aspect, the present application provides a computer-readable medium, where program codes are stored in the computer-readable medium, and when the computer program codes are executed on a computer, the computer is made to execute the implementation of the first aspect above. method.

In a sixth aspect, the present application provides a chip, including: a processing module and a communication interface, where the processing module is configured to execute the method in any possible implementation manner of the above-mentioned first aspect.

The chip also includes a storage module, the storage module is used for storing instructions, the processing module is used for executing the instructions stored in the storage module, and the execution of the instructions stored in the storage module causes the processing module to execute The method in any possible implementation manner of the above first aspect.

Description of drawings

Fig. 1 is the general flow chart of the method for adjusting image quality in this application;

Fig. 2(a) and Fig. 2(b) are determined in the x-axis direction (horizontal direction) using the SAD scanning algorithm in the application to match the j-th rectangular window in the i+1-th image with the i-th sampling image The schematic diagram of the rectangular window;

Fig. 3 is one of the specific flow charts of the processor in the application to determine the motion information corresponding to two adjacent sampled images;

Fig. 4 is the second specific flow chart of the processor in the application to determine the motion information corresponding to two adjacent sampled images;

5 is a schematic structural diagram of an apparatus for adjusting image quality in the present application;

FIG. 6 is a schematic structural diagram of an apparatus for adjusting image quality in the present application.

Detailed ways

The embodiments of the present application will be described below with reference to the accompanying drawings.

The method for adjusting image quality provided in this application can be used to provide motion information for a photographic camera in a mobile electronic device. Further, the mobile electronic device can control the exposure parameters of the photographic camera based on the motion information, for example, reduce the exposure parameters, or use an image post-processing algorithm to compensate or correct the image in combination with the motion information, thereby improving the imaging quality and film yield. The method for adjusting image quality provided in this application may be executed by a processor or a chip in a mobile electronic device.

Referring to Figure 1, the present application provides a method for adjusting image quality, the method comprising:

Step 100: The processor performs sampling processing on the n images continuously obtained in the camera preview process to obtain n sampled images.

Among them, the n images can be small-scale pixel images, the ith image corresponds to the ith sampled image, the resolution of the ith sampled image is lower than that of the ith image, and the ith image is one of the n images. For any image of , i and n are positive integers.

For example, a photographic camera in a mobile electronic device can achieve 30 consecutive images per second during camera preview. The processor performs sampling processing respectively according to the 30 images, that is, sampling processing is performed on each of the 30 images in turn to obtain 30 sampled images.

In order to reduce the computational complexity and power consumption of the processor, reducing the image resolution is the most direct method. For example, it is possible to directly perform downsampling on n images, but directly perform downsampling on n images to obtain n images. Sampling images may cause flickering of the sampled images, resulting in false motion detection. Therefore, the present application proposes a possible sampling method. Taking the ith image as an example, the sampling processing on the ith image is specifically: performing downsampling after preset filtering processing in the horizontal direction of the ith image, and performing downsampling on the ith image in the horizontal direction. Downsampling is performed in the vertical direction of the ith image.

In addition, the sampling processing performed on the ith image may also be: performing downsampling after the first preset filtering processing in the horizontal direction of the ith image, and performing downsampling in the vertical direction of the ith image through a second preset filtering process. It is assumed that downsampling is performed after filtering. The first preset filtering process and the second preset filtering process may be the same or different.

The above-mentioned preset filtering process is an infinite impulse response filter (IIR filter) or a finite impulse response filter (FIR filter), which is not limited in this application.

By adopting the above sampling method, as long as the filtering coefficients are properly designed, the obtained n sampled images can effectively avoid the problem of flickering of the sampled images, and the calculation amount of the processor is low. For example, the total pixels per sampled image are a little under 50K.

Step 110: The processor determines a group of motion regions according to every two adjacent sampled images in the n sampled images, and obtains n-1 groups of motion regions.

Among them, the i-th group of motion regions is obtained according to the i-th sampled image and the i+1-th sampled image, and the i-th group of motion regions includes the motion region corresponding to the i-th sampled image and the i+1-th sampled image. sports area.

For example, suppose there are 4 sampled images in total, and each adjacent two sampled images determine a group of motion areas, that is, the first sampled image and the second sampled image determine the first group of motion areas, the second sampled image and the third sampled image. The second sample image determines the second group of motion regions, the third sample image and the fourth sample image determine the third group of motion regions, and a total of 3 groups of motion regions are obtained.

The first group of motion regions is obtained according to the first sampled image and the second sampled image, and the first group of motion regions includes a motion region corresponding to the first sampled image and a motion region corresponding to the second sampled image. The second group of motion regions is obtained according to the second sampled image and the third sampled image, and the third group of motion regions includes a motion region corresponding to the second sampled image and a motion region corresponding to the third sampled image. The third group of motion areas is obtained according to the third sampled image and the fourth sampled image, and the third group of motion areas includes the motion area corresponding to the third sampled image and the motion area corresponding to the fourth sampled image.

It should be understood that the motion region corresponding to the second sampled image included in the first group of motion regions is different from the motion region corresponding to the second sampled image included in the second group of motion regions, because the first group of motion regions The motion area corresponding to the second sampled image included in the area is obtained from the first sampled image and the second sampled image, and refers to the motion area of the second sampled image relative to the first sampled image in the second sampled image . The motion area corresponding to the second sampled image included in the second group of motion areas is obtained from the second sampled image and the third sampled image, which means that the third sampled image in the second sampled image is relative to the second sampled image. Motion region of the sampled image. Similarly, the motion region corresponding to the third sampled image included in the second group of motion regions is different from the motion region corresponding to the third sampled image included in the third group of motion regions.

Step 120: The processor determines the pixel displacements corresponding to the n-1 groups of motion regions respectively according to the n-1 groups of motion regions.

The pixel displacement corresponding to the i-th group of motion regions refers to the pixel displacement of the motion region corresponding to the i+1-th sampled image relative to the motion region corresponding to the i-th sampled image.

Step 130: The processor determines motion information corresponding to the n-1 groups of motion regions according to the pixel displacements corresponding to the n-1 groups of motion regions respectively.

The motion information corresponding to each of the n-1 groups of motion regions is used to adjust the image quality. For example, the processor may control the exposure parameters of the photographic camera based on the motion information corresponding to the n-1 groups of motion regions, for example, reduce the exposure parameters to reduce image blur caused by moving smears of objects within the exposure time. For another example, the processor may use an image post-processing algorithm to perform compensation or correction processing for the image in combination with the motion information corresponding to the n-1 groups of motion regions respectively. Therefore, by using the motion information corresponding to the n-1 groups of motion regions respectively, the image quality can be improved and the film formation rate can be improved.

For steps 110 to 130, the following takes the i-th sampled image and the i+1-th sampled image as examples to specifically describe how to determine the i-th group of motion regions, the pixel displacements corresponding to the i-th group of motion regions, and the i-th group of motion regions. The motion information corresponding to the region.

First, the processor first determines the i-th group of motion regions.

For the i-th sampled image and the i+1-th sampled image, the processor executes:

(1) The processor divides the i-th sampled image and the i+1-th sampled image into M×N rectangular windows, where M and N are both positive integers.

It should be understood that each rectangular window in the M×N rectangular windows in the i-th sampled image is equal in size to each rectangular window in the M×N rectangular windows in the i-th sampled image, one-to-one correspondence. .

(2) Calculate M×N first correlation coefficients, where the kth first correlation coefficient is the correlation between the kth rectangular window in the ith sampled image and the kth rectangular window in the i+1th sampled image coefficient, the k-th first correlation coefficient is any one of the M×N first correlation coefficients, and k is a positive integer.

The correlation coefficient algorithm for calculating the first correlation coefficient here may be a projection histogram method, an image histogram method, a color feature method, etc., which is not limited in this application.

In a possible design, in order to reduce the computational complexity and power consumption of the processor, the processor may use the projection histogram method to calculate the correlation coefficient of the two rectangular windows. Therefore, the k-th first correlation coefficient can be the projection histogram of the k-th rectangular window in the i-th sampled image in the horizontal direction and the projection of the k-th rectangular window in the i+1-th sampled image in the horizontal direction The correlation coefficient of the histogram, the correlation between the projection histogram of the kth rectangular window in the i-th sampled image in the vertical direction and the projection histogram of the k-th rectangular window in the i+1th sampled image in the vertical direction sum of coefficients.

(3) If the k-th first correlation coefficient is less than or equal to the first preset correlation coefficient threshold, that is, the k-th rectangular window in the i-th sampling image and the k-th rectangular window in the i+1-th sampling image are quite different , it can be judged that the kth rectangular window is a motion window, therefore, the processor determines that the motion region corresponding to the i-th sampled image includes the k-th rectangular window in the i-th sampled image, and the motion corresponding to the i+1-th sampled image The region includes the kth rectangular window in the i+1th sampled image.

Using the above method, the processor traverses M×N first correlation coefficients, compares each first correlation coefficient with the first correlation coefficient, and separates all the first correlation coefficients less than or equal to the first preset correlation coefficient threshold Corresponding rectangular windows are all used as motion windows, and then determine the i-th group of motion regions, wherein, the motion region corresponding to the i+1th sampled image in the i-th sampled image corresponding motion region includes the rectangular window and the i-th group of motion regions. The rectangular windows included in the region correspond one-to-one.

Therefore, through the above method, the processor can determine that there is a moving object in the i+1 th sampled image relative to the ith sampled image, and determine the region where the moving object is located.

Second, the processor determines the pixel displacement corresponding to the i-th group of motion regions.

For the ith group of motion regions, the motion region corresponding to the ith sampled image includes m rectangular windows, and the motion region corresponding to the ith sampled image includes m rectangular windows, and the processor executes:

(1) The processor uses a preset algorithm to calculate m pixel displacements, where the jth pixel displacement is the jth rectangle in the motion region corresponding to the ith sampled image in the motion region corresponding to the i+1th sampled image The pixel displacement of the window-matched rectangular window relative to the j-th rectangular window in the motion region corresponding to the i-th sampled image, where m and j are both positive integers.

The preset algorithm here may be the SAD scanning algorithm, or other algorithms, which are not limited in this application.

As shown in Figure 2(a) and Figure 2(b), in order to use the SAD scanning algorithm to determine in the x-axis direction (horizontal direction) the j-th rectangular window in the i+1-th image and the i-th sampling image Schematic diagram of the matched rectangular window. Fig. 2(a) is the projection histogram of the jth rectangular window in the x-axis direction in the ith sampled image. Fig. 2(b) is the projection histogram corresponding to the x-axis direction from the j-1th rectangular window to the j+1th rectangular window in the i+1th sampled image. The dotted frame is the scanning frame, and its size is the same as that of the rectangular frame. The rectangular frame delineated by the dotted frame in Figure 2(b) is the i+1 image in the x-axis direction and the j-th sampled image in the i-th image. A rectangular window that matches the rectangular window. Therefore, relative to the i+1 th image, the pixel displacement component of the j th rectangular window in the i th sampled image in the x-axis direction can be determined according to the dotted frame.

(2) The processor determines the pixel displacement corresponding to the i-th group of motion regions according to the m pixel displacements.

According to the above m pixel displacements, the average value of pixel displacement components in the x-axis direction and the average value of pixel displacement components in the y-axis direction can be obtained, and then the pixel displacement corresponding to the i-th group of motion regions can be determined.

Second, the processor determines motion information corresponding to the i-th group of motion regions.

The motion information corresponding to the i-th group of motion areas includes at least one of the following: pixel displacement corresponding to the i-th group of motion areas, a motion speed identifier corresponding to the i-th group of motion areas, and a motion direction identifier corresponding to the i-th group of motion areas.

The motion speed identifier corresponding to the ith group of motion regions is a motion speed identifier corresponding to a preset displacement size threshold range within which the size of the pixel displacement corresponding to the ith group of motion regions falls.

For example, 1 pixel to 5 pixels corresponds to motion speed identification 1, 5 to 8 pixels corresponds to motion speed identification 2, and more than 8 pixels corresponds to motion speed identification 3. Assuming that the size of the pixel displacement corresponding to the ith group of motion areas is 3, the motion speed identifier corresponding to the ith group of motion areas is the motion speed identifier 1.

The motion direction identifier corresponding to the ith group of motion areas is a motion direction identifier corresponding to a preset angle range within which the direction of the pixel displacement corresponding to the ith group of motion areas falls.

For example, 360 degrees are divided into 12 angle intervals, each 30 degrees corresponds to a movement direction mark, and there are 12 movement direction marks in total. Assuming that the direction of the pixel displacement corresponding to the ith group of motion areas is 45 degrees, the motion speed identifier corresponding to the ith group of motion areas is motion speed identifier 2.

Therefore, the present application can provide accurate object motion information, and can determine the motion speed and motion direction.

Further, in order to ensure the accuracy of the above results, considering the scanning range corresponding to the SAD scanning algorithm, for example, the scanning range is generally 3 to 5 rectangular windows. Therefore, when the object moves faster and exceeds the scanning range, it may be resulting in inaccurate exercise information. The present application provides a method for judging whether an object is moving at a high speed.

Specifically, after the processor uses the SAD scanning algorithm to calculate the m pixel displacements, if the m pixel displacements satisfy at least one of the following first judgment criterion and second judgment criterion, the motion information corresponding to the i-th group of motion regions includes: The first identifier, where the first identifier is used to indicate that the i-th group of motion areas is in a high-speed motion state.

The first judgment criterion refers to that the number of m second correlation coefficients less than or equal to the second preset correlation coefficient threshold is greater than or equal to the first preset number;

Among them, the jth second correlation coefficient is the rectangular window that matches the jth rectangular window in the motion region corresponding to the ith sampled image in the motion region corresponding to the ith sampled image and the ith sampled image. Correlation coefficient of the jth rectangular window in the motion region.

It should be understood that the algorithm of the second correlation coefficient may be the same as or different from the algorithm of the first correlation coefficient, and the second preset correlation coefficient threshold may be the same as or different from the first preset correlation coefficient threshold, and the repetition will not be repeated.

The second judgment criterion refers to that the number of different directions among the m pixel displacement directions is greater than or equal to the second preset number.

For example, m=10, the number of 10 second correlation coefficients less than or equal to the second preset correlation coefficient threshold is 6 and greater than or equal to the first preset number of 5, and the number of different directions among the 10 pixel displacement directions is 7 If it is greater than or equal to the second preset number of 6, it can be determined that the i-th group of motion areas is in a high-speed motion state, and the motion information corresponding to the i-th group of motion areas includes the first identifier, and other information may not be carried at this time.

Therefore, the above judgment can make up for the deficiency of the calculation error of the SAD scanning algorithm in the fast motion scene, and ensure the accuracy of the object motion information.

In addition, in order to ensure the stability of the above results, in a possible design, before determining the motion information corresponding to the n-1 groups of motion regions, the processor may perform low Pass filtering to eliminate the influence of random noise and singular values on the calculation results.

In another possible design, after determining the motion information corresponding to the ith group of motion regions, if the absolute value of the difference between the size of the pixel displacement corresponding to the ith group of motion regions and the first threshold is less than or equal to the fluctuation threshold, The processor can adjust the first threshold, which eliminates the problem of frequent jittering of the movement speed marker when the magnitude of the displacement of multiple pixels is located near the threshold.

For example, the initial setting of 1 to 5 pixels corresponds to the motion speed flag 1, 5 to 8 pixels corresponds to the motion speed flag 2, the fluctuation threshold is 1, and the size of the pixel displacement corresponding to the first group of motion areas is 4 pixels , 4<5, the motion speed identifier corresponding to the first group of motion regions is motion speed identifier 1. The absolute value of the difference between the size of the pixel displacement corresponding to the first group of motion areas and the upper threshold of the motion speed flag 1 is 1, and 5-4=1. At this time, the absolute value of the difference is equal to the fluctuation threshold 1, then the processor The upper limit of the threshold for adjusting the motion speed flag 1 is 5+1=6. The size of the pixel displacement corresponding to the second group of motion areas is 5 pixels, 5<6, the motion speed corresponding to the second group of motion areas is identified as motion speed flag 1, and the size of the pixel displacement corresponding to the second group of motion areas is far from the latest motion The absolute value of the difference between the upper limit of the threshold value of the speed marker 1 is 1, and 6-5=1. At this time, the absolute value of the difference value is equal to the fluctuation threshold value 1, and the processor adjusts the upper limit of the threshold value of the motion speed marker 1 to 6+1= 7. The size of the pixel displacement corresponding to the third group of motion areas is 5 pixels, 5<7, and the motion speed identifier corresponding to the third group of motion areas is the motion speed identifier 1. The absolute value of the difference between the size of the pixel displacement corresponding to the third group of motion regions and the upper threshold of the latest motion speed indicator 1 is 2, and 7-5=2. At this time, the absolute value of the difference is greater than the fluctuation threshold 1, then The processor does not adjust the upper limit of the threshold value of the motion speed flag 1, and the upper limit of the threshold value of the motion speed flag 1 is maintained at 7.

For another example, the initial setting of 1 to 5 pixels corresponds to the motion speed flag 1, 5 to 8 pixels corresponds to the motion speed flag 2, the fluctuation threshold is 1, and the size of the pixel displacement corresponding to the first group of motion regions is 6 Pixels, 5<6, the first group of motion regions corresponding to the motion speed is identified as the motion speed flag 2. The absolute value of the difference between the size of the pixel displacement corresponding to the first group of motion regions and the upper threshold of the motion speed flag 1 is 1, 6-5=1, and the absolute value of the difference is equal to the fluctuation threshold 1, then the processor The upper limit of the threshold for adjusting the motion speed flag 1 is 5-1=4. In addition, the absolute value of the difference between the size of the pixel displacement corresponding to the first group of motion regions and the upper threshold of the motion speed flag 2 is 2. At this time, the absolute value of the difference is greater than the fluctuation threshold of 1, and the processor does not adjust the motion speed. Identifies the upper threshold of 2. The size of the pixel displacement corresponding to the second group of motion areas is 5 pixels, 4<5, the motion speed corresponding to the second group of motion areas is marked as motion speed mark 2, and the size of the pixel displacement corresponding to the second group of motion areas is far from the latest motion The absolute value of the difference between the upper limit of the threshold value of the speed marker 1 is 1, and 5-4=1. At this time, the absolute value of the difference is equal to the fluctuation threshold value 1, and the processor adjusts the upper limit of the threshold value of the motion speed marker 1 to 4-1= 3. The size of the pixel displacement corresponding to the third group of motion areas is 5 pixels, 3<5, and the motion speed identifier corresponding to the third group of motion areas is motion speed identifier 2. The absolute value of the difference between the size of the pixel displacement corresponding to the third group of motion areas and the upper threshold of the latest motion speed indicator 1 is 2, 5-3=2, and the absolute value of the difference is greater than the fluctuation threshold 1, then The processor does not adjust the upper limit of the threshold value of the motion speed flag 1, and the upper limit of the threshold value of the motion speed flag 1 is maintained at 3.

Therefore, by dynamically adjusting the threshold, it is possible to eliminate frequent jitters of the motion speed markers that occur when the displacement of multiple pixels is located near the threshold, thereby ensuring the stability of the output result.

Referring to FIG. 3 , it is one of the specific flowcharts for the processor to determine motion information corresponding to two adjacent sampled images.

S301 : Divide the two adjacent sampling images into M×N rectangular windows, respectively, and create projection histograms in the x-axis direction and the y-axis direction for each rectangular window in each sampled image.

S302: Calculate M×N first correlation coefficients.

Wherein, each first correlation coefficient is the sum of the correlation coefficient of the projected histogram of the rectangular window at the same position in the two adjacent sampling images in the horizontal direction and the correlation coefficient of the projected histogram in the vertical direction.

S303: Determine whether the current rectangular window is a motion window, if the first correlation coefficient corresponding to the current rectangular window is less than or equal to the first preset correlation coefficient threshold, execute S304, and if the first correlation coefficient corresponding to the current rectangular window is greater than the first preset correlation Coefficient threshold, execute S305.

S304: Calculate the pixel displacement corresponding to the current rectangular window.

S305: Determine whether the current rectangular window is the last rectangular window, and if so, execute 306; otherwise, move the rectangular window indicated by the current rectangular window to the next rectangular window, and return to S303.

S306: Synthesize the pixel displacements corresponding to all the motion windows and output the motion speed identifiers and the corresponding motion direction identifiers corresponding to the two adjacent sampling images.

With the embodiment shown in FIG. 3 , the processor can detect object motion with low computational complexity and low power consumption, and determine motion information corresponding to two adjacent sampled images.

Referring to FIG. 4 , it is the second specific flow chart for the processor to determine the motion information corresponding to two adjacent sampled images.

S401: Divide the two adjacent sampling images into M×N rectangular windows respectively.

S402: Determine the number of motion windows to be m, and the displacement of m pixels.

where m is a positive integer.

S403: Calculate m second correlation coefficients.

S404: Count the number of different directions in the directions of m pixel displacements.

S405: Determine whether the number of m second correlation coefficients less than or equal to the second preset correlation coefficient threshold is greater than or equal to the first preset number, and whether the number of different directions in the directions of m pixel displacements is greater than or equal to the second preset number , if yes, go to S406, otherwise go to S407.

S406: Output the first identifier, and end the process.

S407: Combine the m pixel displacements and output the motion speed identifiers and the corresponding motion direction identifiers corresponding to the two adjacent sampling images.

Therefore, through the embodiment shown in FIG. 4 , the results obtained by the embodiment shown in FIG. 3 can be verified, thereby making up for the insufficiency of the calculation error of the SAD scanning algorithm in the fast motion scene, and ensuring the accuracy of the object motion information.

Referring to FIG. 5 , the present application provides an apparatus 500 for adjusting image quality, which includes:

The sampling unit 501 is configured to perform sampling processing on n images continuously obtained in the camera preview process, respectively, to obtain n sampled images, wherein the ith image corresponds to the ith sampled image, and the ith image is the For any one of the n images, i and n are positive integers;

The processing unit 502 is configured to determine a group of motion regions according to every two adjacent sampled images in the n sampled images, and obtain an n-1 group of motion regions, wherein the i-th group of motion regions is based on the i-th sampled images. The image and the i+1 th sampled image are obtained, and the i-th group of motion regions includes a motion region corresponding to the i-th sampled image and a motion region corresponding to the i+1-th sampled image;

The calculation unit 503 is configured to determine the pixel displacements corresponding to the n-1 groups of motion regions according to the n-1 groups of motion regions, wherein the pixel displacement corresponding to the i-th group of motion regions refers to the i-th group of motion regions. +1 pixel displacement of the motion region corresponding to the sampled image relative to the motion region corresponding to the i-th sampled image;

The analysis unit 504 is configured to determine the motion information corresponding to the n-1 groups of motion areas according to the pixel displacements corresponding to the n-1 groups of motion areas respectively, and the motion information corresponding to the n-1 groups of motion areas is used for Adjust image quality.

In a possible design, the i-th sampled image refers to performing downsampling after a preset filtering process in the horizontal direction of the i-th image, and in the vertical direction of the i-th image Image obtained after performing downsampling.

In a possible design, the preset filtering process is an infinite impulse response filter IIR filter, or a finite impulse response filter FIR filter.

In a possible design, when a group of motion regions is determined according to every two adjacent sampled images in the n sampled images, the processing unit 502 is specifically configured to:

For the i-th sampled image and the i+1-th sampled image, execute:

Both the i-th sampled image and the i+1-th sampled image are divided into M×N rectangular windows, where M and N are both positive integers;

Calculate M×N first correlation coefficients, where the k-th first correlation coefficient is the difference between the k-th rectangular window in the i-th sampled image and the k-th rectangular window in the i+1-th sampled image a correlation coefficient, the kth first correlation coefficient is any one of the M×N first correlation coefficients, and k is a positive integer;

The ith group of motion regions is determined according to the rectangular window corresponding to the first correlation coefficient less than or equal to the first preset correlation coefficient threshold among the M×N first correlation coefficients, wherein the ith sampled image corresponds to The rectangular window included in the motion region of t corresponds to the rectangular window included in the motion region corresponding to the i+1 th sampled image in the i th group of motion regions in a one-to-one correspondence.

In a possible design, the k-th first correlation coefficient is the projection histogram of the k-th rectangular window in the i-th sampled image in the horizontal direction and the i+1-th sampled image The correlation coefficient of the projection histogram of the kth rectangular window in the horizontal direction and the projection histogram of the kth rectangular window in the vertical direction in the i-th sampling image and the i+1-th sampling image The sum of the correlation coefficients of the projected histograms of the kth rectangular window in the vertical direction.

In a possible design, when determining the pixel displacements corresponding to the n-1 groups of motion regions according to the n-1 groups of motion regions, the calculation unit 503 is specifically used for:

For the i-th group of motion regions, the motion region corresponding to the i-th sampled image includes m rectangular windows, and the motion region corresponding to the i+1-th sampled image includes m rectangular windows, and execute:

A preset algorithm is used to calculate m pixel displacements, where the jth pixel displacement is the jth rectangular window in the motion region corresponding to the ith sampled image in the motion region corresponding to the i+1th sampled image The pixel displacement of the jth rectangular window in the motion region corresponding to the i-th sampled image of the matched rectangular window, where m and j are both positive integers;

The pixel displacement corresponding to the i-th group of motion regions is determined according to the m pixel displacements.

In a possible design, the motion information corresponding to the ith group of motion areas includes at least one of the following: a pixel displacement corresponding to the ith group of motion areas, a motion speed identifier corresponding to the ith group of motion areas, The motion direction identification corresponding to the i-th group of motion areas;

Wherein, the motion speed identifier corresponding to the i-th group of motion areas is a motion speed identifier corresponding to a preset displacement size threshold range that falls according to the size of the pixel displacement corresponding to the i-th group of motion areas;

The motion direction identifier corresponding to the i-th group of motion areas is a motion direction identifier corresponding to a preset angle range within which the direction of the pixel displacement corresponding to the i-th group of motion areas falls.

In a possible design, if the m pixel displacements satisfy at least one of the following first judgment criterion and second judgment criterion, the motion information corresponding to the i-th group of motion regions includes a first identifier, and the i-th group of motion regions A flag is used to indicate that the i-th group of motion areas is in a high-speed motion state;

The first judgment criterion refers to that the number of m second correlation coefficients less than or equal to the second preset correlation coefficient threshold is greater than or equal to the first preset number;

Wherein, the jth second correlation coefficient is the rectangular window that matches the jth rectangular window in the motion region corresponding to the i+1th sampled image and the jth rectangular window in the motion region corresponding to the ith sampled image and the The correlation coefficient of the jth rectangular window in the motion region corresponding to the i sampled images.

The second judgment criterion refers to that the number of different directions in the displacement directions of the m pixels is greater than or equal to a second preset number.

In a possible design, before determining the motion information corresponding to the n-1 groups of motion regions, the analysis unit 504 is further configured to:

Perform low-pass filtering on the pixel displacements corresponding to the n-1 groups of motion regions respectively.

In a possible design, after determining the motion information corresponding to the n-1 groups of motion regions, the analysis unit 504 is further configured to:

If the absolute value of the difference between the size of the pixel displacement corresponding to the n-1 groups of motion regions and the first threshold value is less than or equal to the fluctuation threshold value, the first threshold value is adjusted.

It should be understood that the division of the above units is only a division of logical functions, and in actual implementation, all or part of them may be integrated into a physical entity, or they may be physically separated. And these units can all be implemented in the form of software calling through the processing element; also can all be implemented in the form of hardware; some units can also be implemented in the form of software calling through the processing element, and some units can be implemented in the form of hardware. For example, the processing unit can be a separately established processing element, or can be integrated in a certain chip to realize, in addition, it can also be stored in the memory in the form of a program, and the function of the unit can be called and executed by a certain processing element. The implementation of other units is similar. In addition, all or part of these units can be integrated together, and can also be implemented independently. The processing element described here may be an integrated circuit with signal processing capability. In the implementation process, each step of the above-mentioned method or each of the above-mentioned units may be completed by an integrated logic circuit of hardware in the processor element or an instruction in the form of software.

Based on the above embodiments, referring to FIG. 6 , an embodiment of the present application further provides a device 600 for adjusting image quality. The device 600 for adjusting image quality includes: a communication interface 601 , a processor 602 , and a memory 603 . The above-mentioned consecutively obtained n images are obtained through the communication interface 601, the motion information determined by the analysis unit can be output through the communication interface 601, the functions of the sampling unit, the function of the processing unit, the function of the calculation unit and the function of the analysis unit can be passed through The processor 602 is implemented.

The memory 603 is used to store computer program codes. The memory 603 may include random access memory (Random Access Memory, RAM), etc., and may also include non-volatile memory, such as at least one disk storage. The processor 602 may be a central processing unit (central processing unit, CPU), a network processor (network processor, NP), a hardware chip or any combination thereof. The processor 602 executes the computer program codes stored in the memory 603 to implement the method shown in FIG. 1 .

To sum up, using the method for adjusting image quality provided by the present application, the processor performs sampling processing on n images continuously obtained during the camera preview process, respectively, to obtain n sampled images. A low-resolution sampled image is obtained through sampling processing. The processor determines a group of motion regions according to every two adjacent sampled images in the n sampled images, and obtains n-1 groups of motion regions, that is, the motion regions are identified. For the n-1 groups of motion areas, determine the pixel displacements corresponding to the n-1 groups of motion areas respectively, and determine the corresponding pixel displacements of the n-1 groups of motion areas according to the respective pixel displacements of the n-1 groups of motion areas. Motion information, the motion information respectively corresponding to the n-1 groups of motion regions is used to adjust the image quality.

Therefore, through the above method, it is possible to reduce the image resolution through sampling processing, obtain a sampled image, and determine motion information according to the sampled image, so that the obtained motion information is used to adjust the image quality. This method has the characteristics of low computational complexity and low power consumption.

Those skilled in the art should understand that the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The embodiments of the present application are described with reference to flowcharts and/or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims (18)

1. A method for adjusting image quality, the method comprising:

respectively performing sampling processing on n images continuously obtained in a camera previewing process to obtain n sampled images, wherein the ith image corresponds to the ith sampled image, the resolution of the ith sampled image is lower than that of the ith image, the ith image is any one of the n images, and i and n are positive integers; the ith sampling image is an image obtained by performing down-sampling on the ith image after the ith image is subjected to preset filtering processing in the horizontal direction and performing down-sampling in the vertical direction of the ith image;

determining a group of motion areas according to every two adjacent sampling images in the n sampling images to obtain n-1 groups of motion areas, wherein the ith group of motion areas is obtained according to the ith sampling image and the (i + 1) th sampling image, and the ith group of motion areas comprises the motion area corresponding to the ith sampling image and the motion area corresponding to the (i + 1) th sampling image;

determining pixel displacement corresponding to the n-1 groups of motion areas according to the n-1 groups of motion areas, wherein the pixel displacement corresponding to the i group of motion areas refers to the pixel displacement of the motion area corresponding to the (i + 1) th sampling image relative to the motion area corresponding to the i sample image;

and determining motion information corresponding to the n-1 groups of motion areas according to the pixel displacement corresponding to the n-1 groups of motion areas respectively, wherein the motion information corresponding to the n-1 groups of motion areas respectively is used for adjusting the image quality.

2. The method of claim 1, wherein the predetermined filtering process is an Infinite Impulse Response (IIR) filter or a Finite Impulse Response (FIR) filter.

3. The method of claim 1 or 2, wherein determining a set of motion regions from every two adjacent ones of the n sampled images comprises:

for the ith sample image and the (i + 1) th sample image, executing:

dividing the ith sampling image and the (i + 1) th sampling image into M multiplied by N rectangular windows, wherein M and N are positive integers;

calculating M × N first correlation coefficients, wherein a kth first correlation coefficient is a correlation coefficient of a kth rectangular window in the ith sampling image and a kth rectangular window in the (i + 1) th sampling image, the kth first correlation coefficient is any one of the M × N first correlation coefficients, and k is a positive integer;

and determining the ith group of motion areas according to rectangular windows corresponding to first correlation coefficients which are less than or equal to a first preset correlation coefficient threshold value in the M multiplied by N first correlation coefficients, wherein the rectangular windows included in the motion areas corresponding to the ith sampling image correspond to the rectangular windows included in the motion areas corresponding to the (i + 1) th sampling image in the ith group of motion areas in a one-to-one mode.

4. The method according to claim 3, wherein the kth first correlation coefficient is a sum of a correlation coefficient of a projection histogram of a kth rectangular window in the i-th sampled image in the horizontal direction and a projection histogram of a kth rectangular window in the i + 1-th sampled image in the horizontal direction, and a correlation coefficient of a projection histogram of a kth rectangular window in the i-th sampled image in the vertical direction and a projection histogram of a kth rectangular window in the i + 1-th sampled image in the vertical direction.

5. The method of claim 3, wherein determining the pixel displacement corresponding to each of the n-1 sets of motion regions according to the n-1 sets of motion regions comprises:

for the ith group of motion areas, the motion area corresponding to the ith sampling image comprises m rectangular windows, and the motion area corresponding to the (i + 1) th sampling image comprises m rectangular windows, executing:

calculating m pixel displacements by adopting a preset algorithm, wherein the jth pixel displacement is the pixel displacement of a jth rectangular window in a motion region corresponding to the (i + 1) th sampling image, which is matched with the jth rectangular window in the motion region corresponding to the ith sampling image, relative to the jth rectangular window in the motion region corresponding to the ith sampling image, and m and j are positive integers;

and determining pixel displacement corresponding to the ith group of motion areas according to the m pixel displacements.

6. The method of claim 5, wherein the motion information corresponding to the i-th group of motion areas comprises at least one of: pixel displacement corresponding to the ith group of motion areas, motion speed identification corresponding to the ith group of motion areas and motion direction identification corresponding to the ith group of motion areas;

the motion speed identifier corresponding to the ith group of motion areas is a motion speed identifier corresponding to a preset displacement threshold range in which the pixel displacement corresponding to the ith group of motion areas falls;

the motion direction identifier corresponding to the ith group of motion areas is a motion direction identifier corresponding to a preset angle range in which the pixel displacement direction corresponding to the ith group of motion areas falls.

7. The method according to claim 5, wherein if the m pixel displacements satisfy at least one of the following first and second determination criteria, the motion information corresponding to the i-th group of motion areas includes a first flag indicating that the i-th group of motion areas is in a high-speed motion state;

the first judgment criterion means that the number of the m second correlation coefficients which is less than or equal to a second preset correlation coefficient threshold value is greater than or equal to a first preset number;

wherein, the jth second correlation coefficient is a correlation coefficient of a jth rectangular window in a motion region corresponding to the i +1 th sampling image, which is matched with the jth rectangular window in the motion region corresponding to the i th sampling image, and a jth rectangular window in the motion region corresponding to the i th sampling image;

the second judgment criterion is that the number of different directions in the m pixel displacement directions is greater than or equal to a second preset number.

8. The method according to claim 1 or 2, wherein before determining the motion information corresponding to the n-1 sets of motion areas, respectively, further comprising:

and performing low-pass filtering on the pixel displacement corresponding to the n-1 groups of motion areas respectively.

9. The method according to claim 1 or 2, wherein after determining the motion information corresponding to the i-th group of motion areas, further comprising:

and if the absolute value of the difference value between the pixel displacement corresponding to the i groups of motion areas and a first threshold value is less than or equal to a fluctuation threshold value, adjusting the first threshold value.

10. An apparatus for adjusting image quality, the apparatus comprising:

the device comprises a sampling unit, a processing unit and a processing unit, wherein the sampling unit is used for respectively carrying out sampling processing on n images continuously obtained in the process of previewing a camera to obtain n sampled images, the ith image corresponds to the ith sampled image, the resolution of the ith sampled image is lower than that of the ith image, the ith image is any one of the n images, and i and n are positive integers; the ith sampling image is an image obtained by performing down-sampling on the ith image after the ith image is subjected to preset filtering processing in the horizontal direction and performing down-sampling in the vertical direction of the ith image;

the processing unit is used for determining a group of motion areas according to every two adjacent sampling images in the n sampling images to obtain n-1 groups of motion areas, wherein the ith group of motion areas is obtained according to the ith sampling image and the (i + 1) th sampling image, and the ith group of motion areas comprises the motion area corresponding to the ith sampling image and the motion area corresponding to the (i + 1) th sampling image;

a calculating unit, configured to determine, according to the n-1 sets of motion areas, pixel displacements corresponding to the n-1 sets of motion areas, respectively, where the pixel displacement corresponding to the i-th set of motion areas refers to a pixel displacement of a motion area corresponding to the (i + 1) -th sampling image relative to a motion area corresponding to the i-th sampling image;

and the analysis unit is used for determining the motion information corresponding to the n-1 groups of motion areas according to the pixel displacement corresponding to the n-1 groups of motion areas respectively, and the motion information corresponding to the n-1 groups of motion areas respectively is used for adjusting the image quality.

11. The apparatus of claim 10, wherein the predetermined filtering process is an Infinite Impulse Response (IIR) filter or a Finite Impulse Response (FIR) filter.

12. The apparatus according to claim 10 or 11, wherein when determining a group of motion regions from every two adjacent sampled images of the n sampled images, the processing unit is specifically configured to:

for the ith sample image and the (i + 1) th sample image, executing:

dividing the ith sampling image and the (i + 1) th sampling image into M multiplied by N rectangular windows, wherein M and N are positive integers;

calculating M × N first correlation coefficients, wherein a kth first correlation coefficient is a correlation coefficient of a kth rectangular window in the ith sampling image and a kth rectangular window in the (i + 1) th sampling image, the kth first correlation coefficient is any one of the M × N first correlation coefficients, and k is a positive integer;

and determining the ith group of motion areas according to rectangular windows corresponding to first correlation coefficients which are less than or equal to a first preset correlation coefficient threshold value in the M multiplied by N first correlation coefficients, wherein the rectangular windows included in the motion areas corresponding to the ith sampling image correspond to the rectangular windows included in the motion areas corresponding to the (i + 1) th sampling image in the ith group of motion areas in a one-to-one mode.

13. The apparatus according to claim 12, wherein the kth first correlation coefficient is a sum of a correlation coefficient of a projection histogram of a kth rectangular window in the i-th sampled image in a horizontal direction and a projection histogram of a kth rectangular window in the i + 1-th sampled image in the horizontal direction, and a correlation coefficient of a projection histogram of a kth rectangular window in the i-th sampled image in a vertical direction and a projection histogram of a kth rectangular window in the i + 1-th sampled image in the vertical direction.

14. The apparatus according to claim 12, wherein when determining, according to the n-1 sets of motion regions, pixel displacements corresponding to the n-1 sets of motion regions, the computing unit is specifically configured to:

for the ith group of motion areas, the motion area corresponding to the ith sampling image comprises m rectangular windows, and the motion area corresponding to the (i + 1) th sampling image comprises m rectangular windows, executing:

calculating m pixel displacements by adopting a preset algorithm, wherein the jth pixel displacement is the pixel displacement of a jth rectangular window in a motion region corresponding to the (i + 1) th sampling image, which is matched with the jth rectangular window in the motion region corresponding to the ith sampling image, relative to the jth rectangular window in the motion region corresponding to the ith sampling image, and m and j are positive integers;

and determining pixel displacement corresponding to the ith group of motion areas according to the m pixel displacements.

15. The apparatus of claim 14, wherein the motion information corresponding to the i-th group of motion regions comprises at least one of: pixel displacement corresponding to the ith group of motion areas, motion speed identification corresponding to the ith group of motion areas and motion direction identification corresponding to the ith group of motion areas;

the motion speed identifier corresponding to the ith group of motion areas is a motion speed identifier corresponding to a preset displacement threshold range in which the pixel displacement corresponding to the ith group of motion areas falls;

the motion direction identifier corresponding to the ith group of motion areas is a motion direction identifier corresponding to a preset angle range in which the pixel displacement direction corresponding to the ith group of motion areas falls.

16. The apparatus according to claim 14, wherein if the m pixel displacements satisfy at least one of the following first and second determination criteria, the motion information corresponding to the i-th group of motion areas includes a first flag indicating that the i-th group of motion areas is in a high-speed motion state;

the first judgment criterion means that the number of the m second correlation coefficients which is less than or equal to a second preset correlation coefficient threshold value is greater than or equal to a first preset number;

wherein, the jth second correlation coefficient is a correlation coefficient of a jth rectangular window in a motion region corresponding to the i +1 th sampling image, which is matched with the jth rectangular window in the motion region corresponding to the i th sampling image, and a jth rectangular window in the motion region corresponding to the i th sampling image;

the second judgment criterion is that the number of different directions in the m pixel displacement directions is greater than or equal to a second preset number.

17. The apparatus according to claim 10 or 11, wherein, before determining the motion information corresponding to the n-1 sets of motion areas, respectively, the analyzing unit is further configured to:

and performing low-pass filtering on the pixel displacement corresponding to the n-1 groups of motion areas respectively.

18. The apparatus according to claim 10 or 11, wherein after determining the motion information corresponding to the i-th group of motion areas, the analyzing unit is further configured to:

and if the absolute value of the difference value between the pixel displacement corresponding to the i groups of motion areas and a first threshold value is less than or equal to a fluctuation threshold value, adjusting the first threshold value.

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