A method and system for remote transmission of nystagmus video of vertigo patients

Abstract

The present invention relates to the field of video transmission technology, and to a remote transmission method and system for nystagmus videos of vertigo patients, comprising: using the change in the number of grayscale pixels between each local eye area corresponding to the i-th frame and the i-1-th frame of nystagmus video frames and each corresponding comparison local eye area to respectively determine the nystagmus features contained in the grayscale in each local eye area; according to the above-mentioned nystagmus features and the number of grayscale pixels in each compression result and in each local eye area, determine the nystagmus detail loss of each local eye area for each compression result; using the nystagmus coefficient to respectively correct the corresponding nystagmus detail loss to obtain the nystagmus detail loss; using the compression result with the nystagmus detail loss less than the preset loss threshold as the target compression result of each local eye area and transmitting it. The present invention can ensure the information integrity of the compressed video frame.

Description

Technical Field

[0001] The present invention relates to the technical field of video transmission, and in particular to a method and system for remotely transmitting video of nystagmus of a vertigo patient. Background Art

[0002] Nystagmus is a common sign in patients with vertigo, characterized by rapid, involuntary eye movements. Detection and analysis of nystagmus is important for the diagnosis and management of vertigo. By recording and analyzing nystagmus on video, doctors can obtain critical diagnostic information to help determine the cause of vertigo and its severity.

[0003] Nystagmus video will compress each frame of nystagmus data before transmission. However, when doctors diagnose patients through real-time nystagmus video, they need high-resolution video. However, transmitting the whole frame compressed video will result in a large amount of nystagmus detail information being lost. Summary of the invention

[0004] In order to solve the technical problem that a large amount of nystagmus detail information will be lost after the whole frame of compressed video, the purpose of the present invention is to provide a method and system for remote transmission of nystagmus video of vertigo patients, and the technical solution adopted is as follows:

[0005] The i-th nystagmus video frame and the i-1-th nystagmus video frame are divided respectively to obtain each eyeball local area of ​​the i-th nystagmus video frame and each comparison eyeball local area of ​​the i-1-th nystagmus video frame, and N compression results of each eyeball local area under different compression ratios are determined;

[0006] At least using the change in the number of pixels of M gray levels between each eyeball local area and each comparison eyeball local area, determine the nystagmus features of the M gray levels in each eyeball local area respectively;

[0007] Determine N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results according to the nystagmus features contained in the M gray levels in each local area of ​​the eyeball, the number of pixels of the M gray levels in the N compression results, and the number of pixels of the M gray levels in each local area of ​​the eyeball;

[0008] Using the nystagmus coefficient of each local area of ​​the eyeball, respectively correct the corresponding N nystagmus detail loss degrees, and obtain N nystagmus detail loss degrees of each local area of ​​the eyeball for N compression results;

[0009] The compression results with N nystagmus detail loss degrees less than a preset loss degree threshold are determined as target compression results of each local eye area, and the i-th nystagmus video frame is transmitted according to the target compression results of each local eye area; wherein M, N and i are positive integers.

[0010] Preferably, at least according to the change of the number of pixels of M gray levels between each local area of ​​the eyeball and each comparative local area of ​​the eyeball, the nystagmus features of the M gray levels in each local area of ​​the eyeball are determined respectively, including:

[0011] According to the change of the number of pixels of M gray levels between each local area of ​​the eyeball and each comparative local area of ​​the eyeball, respectively determine the important factors of the M gray levels in each local area of ​​the eyeball;

[0012] By using the preset nystagmus factors of the M gray levels in each local area of ​​the eyeball, the important factors of the M gray levels in each local area of ​​the eyeball are corrected respectively to obtain the nystagmus characteristics contained in the M gray levels in each local area of ​​the eyeball.

[0013] Preferably, according to the change in the number of pixels of the M gray levels between each eyeball local area and each comparison eyeball local area, the important factors of the M gray levels in each eyeball local area are determined respectively, including:

[0014] Performing region extraction on M gray levels in each eyeball local region and each comparison eyeball local region respectively, to obtain an eyeball sub-region corresponding to each eyeball local region and a comparison eyeball sub-region corresponding to each comparison eyeball local region;

[0015] According to the change in the number of pixels of M gray levels between the eyeball sub-areas corresponding to each eyeball local area and the contrasting eyeball sub-areas corresponding to each contrasting eyeball local area, the important factors of the M gray levels in each eyeball local area are determined respectively.

[0016] Preferably, according to the change in the number of pixels of M gray levels between the eyeball sub-areas corresponding to each eyeball local area and the contrast eyeball sub-areas corresponding to each contrast eyeball local area, the important factors of the M gray levels in each eyeball local area are determined respectively, including:

[0017] Determine a pixel change value according to the number of pixels of the jth gray level in the eyeball sub-region of the kth eyeball local region and the number of pixels of the jth gray level in the contrast eyeball sub-region of the kth contrast eyeball local region;

[0018] According to the pixel change value and the gradient mean of all pixel points corresponding to the jth gray level in the kth local area of ​​the eye, the important factor of the jth gray level in the kth local area of ​​the eye is determined; wherein k is a positive integer and j is a positive integer not greater than M.

[0019] Preferably, determining N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results according to the nystagmus features contained in the M gray levels in each local area of ​​the eyeball, the number of pixels of the M gray levels in the N compression results, and the number of pixels of the M gray levels in each local area of ​​the eyeball comprises:

[0020] Calculate the ratio of the number of pixels of the j-th gray level in the k-th local area of ​​the eyeball to the number of pixels of the j-th gray level in the v-th compression result, and obtain the pixel reduction parameter of the j-th gray level in the v-th compression result;

[0021] Multiplying the pixel reduction parameter of the j-th gray level in the v-th compression result by the nystagmus feature contained in each local area of ​​the eyeball of the j-th gray level, to obtain the sub-nystagmus detail loss degree of the j-th gray level in the v-th compression result;

[0022] The sub-tremor detail loss degrees of the M gray levels in the v-th compression result are summed to obtain the tremor detail loss degree of the k-th local area of ​​the eyeball for the v-th compression result, where v is a positive integer not greater than N.

[0023] Preferably, the method for determining the tremor factor of the preset M gray levels in each local area of ​​the eyeball includes:

[0024] In the case of using the kth eyeball local area of ​​the jth grayscale level for region extraction, respectively calculating the centroid distance between the eyeball sub-area of ​​the kth eyeball local area in the i-th nystagmus video frame and the contrast eyeball sub-area of ​​the kth contrast eyeball local area in the nystagmus video frames between the i-1th frame and the itth frame, to obtain the centroid distance corresponding to the nystagmus video frame;

[0025] Generate centroid coordinates according to centroid distances corresponding to nystagmus video frames corresponding to times of nystagmus video frames between the i-1th frame and the itth frame;

[0026] The tremor factor of the jth gray level in the kth local area of ​​the eyeball is determined according to the amplitude of the frequency spectrum corresponding to the centroid coordinates; wherein t is a positive integer less than i.

[0027] Preferably, determining N compression results of each eyeball local area at different compression ratios includes:

[0028] Downsampling is performed on each local area of ​​the eyeball according to different compression ratios to obtain N compression results corresponding to each local area of ​​the eyeball.

[0029] Preferably, the method for determining the nystagmus coefficient of each local area of ​​the eyeball includes:

[0030] Structural similarity calculations are performed on the kth eyeball local region in the i-th nystagmus video frame and the kth comparative eyeball local region in the nystagmus video frames between the i-1-th frame and the it-th frame, respectively, to obtain t+1 structural similarity parameters;

[0031] The nystagmus coefficient of the kth local area of ​​the eyeball is determined according to the t+1 structural similarity parameters.

[0032] Preferably, the compression results with a detail loss degree less than a preset loss degree threshold among the N nystagmus details are determined as the target compression results of each local area of ​​the eyeball, including:

[0033] The compression results with N nystagmus detail loss degrees less than a preset loss degree threshold are determined as candidate compression results; and the candidate compression result with the highest compression ratio is selected from the candidate compression results as the target compression result of each local area of ​​the eyeball.

[0034] The present invention also provides a remote transmission system for nystagmus video of vertigo patients, comprising:

[0035] A division module is used to divide the i-th nystagmus video frame and the i-1-th nystagmus video frame respectively, obtain each eyeball local area of ​​the i-th nystagmus video frame and each comparison eyeball local area of ​​the i-1-th nystagmus video frame, and determine N compression results of each eyeball local area under different compression ratios;

[0036] A first processing module is used to determine the nystagmus features of the M gray levels in each local area of ​​the eyeball by at least using the change in the number of pixels of the M gray levels between each local area of ​​the eyeball and each comparative local area of ​​the eyeball;

[0037] The second processing module is used to determine N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results according to the nystagmus features contained in the M gray levels in each local area of ​​the eyeball, the number of pixels of the M gray levels in the N compression results, and the number of pixels of the M gray levels in each local area of ​​the eyeball;

[0038] The third processing module is used to respectively correct the corresponding N nystagmus detail loss degrees using the nystagmus coefficient of each local area of ​​the eyeball, and obtain N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results;

[0039] The transmission module is used to determine the compression results of N nystagmus detail loss degrees that are less than a preset loss degree threshold as the target compression results of each local area of ​​the eyeball, and transmit the i-th frame of nystagmus video frame according to the target compression results of each local area of ​​the eyeball; wherein M, N and i are positive integers.

[0040] The present invention has the following beneficial effects:

[0041] The i-th frame of nystagmus video frame and the i-1-th frame of nystagmus video frame are divided respectively to obtain each local eye area of ​​the i-th frame of nystagmus video frame and each comparative local eye area of ​​the i-1-th frame of nystagmus video frame, and N compression results of each local eye area under different compression ratios are determined; at least the change in the number of pixels of M gray levels between each local eye area and each comparative local eye area is used to determine the nystagmus features contained in the M gray levels in each local eye area; according to the determined nystagmus features contained in the M gray levels in each local eye area, the M gray levels in the local eye area are compressed. The number of pixels in the N compression results and the number of pixels in the M gray levels in each local area of ​​the eyeball are used to determine the N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results; the corresponding N nystagmus detail loss degrees are respectively corrected by using the nystagmus coefficient of each local area of ​​the eyeball to obtain the N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results; the compression result with a nystagmus detail loss degree less than a preset loss degree threshold among the N nystagmus detail loss degrees is determined as the target compression result of each local area of ​​the eyeball, and the i-th nystagmus video frame is transmitted according to the target compression result of each local area of ​​the eyeball. In this way, by determining the nystagmus detail loss degree of each local area of ​​the eyeball for different compression results, each local area of ​​the eyeball selects its own compression result, avoiding the whole frame data from being compressed with the same compression ratio, and each local area of ​​the eyeball can select a compression result that retains the detail features of the nystagmus, thereby ensuring the integrity of the information in the compression result, thereby effectively transmitting the nystagmus video frame. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A method flow chart of a method for remotely transmitting nystagmus video of a vertigo patient provided by one embodiment of the present invention;

[0044] Figure 2 A specific method flow chart of step S120 of a method for remotely transmitting video of nystagmus of a vertigo patient provided by one embodiment of the present invention;

[0045] Figure 3 A specific method flow chart of step S130 of a method for remotely transmitting video of nystagmus of a vertigo patient provided by one embodiment of the present invention;

[0046] Figure 4 A schematic diagram of a remote transmission system for nystagmus video of vertigo patients provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method and system for remote transmission of nystagmus video of vertigo patients proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The specific scheme of the method for remote transmission of nystagmus video of vertigo patients provided by the present invention is described in detail below with reference to the accompanying drawings.

[0050] See also Figure 1 , which shows a method flow chart of a method for remote transmission of nystagmus video of a vertigo patient provided by an embodiment of the present invention. In an exemplary embodiment, a method for remote transmission of nystagmus video of a vertigo patient is provided, comprising:

[0051] S110, dividing the i-th nystagmus video frame and the i-1-th nystagmus video frame respectively, obtaining each eyeball local area of ​​the i-th nystagmus video frame and each comparison eyeball local area of ​​the i-1-th nystagmus video frame, and determining N compression results of each eyeball local area under different compression ratios;

[0052] S120, using at least the change in the number of pixels of M gray levels between each eyeball local area and each comparison eyeball local area, respectively determine the nystagmus features of the M gray levels in each eyeball local area;

[0053] S130, determining N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results according to the nystagmus features contained in the M gray levels in each local area of ​​the eyeball, the number of pixels of the M gray levels in the N compression results, and the number of pixels of the M gray levels in each local area of ​​the eyeball;

[0054] S140, using the nystagmus coefficient of each local area of ​​the eyeball, respectively correcting the corresponding N nystagmus detail loss degrees, to obtain N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results;

[0055] S150. Determine the compression results of N nystagmus detail loss degrees that are less than a preset loss degree threshold as the target compression results of each local eye area, and transmit the i-th nystagmus video frame according to the target compression results of each local eye area; wherein M, N and i are positive integers.

[0056] In step S110, exemplarily, the nystagmus video frame is any frame in the nystagmus video, and may also be a specified frame, which is not limited here. In this embodiment, a high-definition camera is used to collect the nystagmus video. That is, the nystagmus video of the patient is collected under corresponding requirements (such as looking straight ahead, looking sideways, and staring at a target).

[0057] Exemplarily, since the nystagmus detail features contained in different positions in each frame of data in the nystagmus video are different, if each frame of data is directly compressed as a whole, a large amount of nystagmus details will be lost. Therefore, it is necessary to divide each frame of data into regions and further analyze the detail feature content contained in each frame of data. Specifically, the video frames (i.e., the i-th frame of nystagmus video frame and the i-1-th frame of nystagmus video frame) are divided according to the preset area. For example, each frame of data in the nystagmus video is divided into several regions of size a×b, where a is the preset region side length, and the specific values ​​of a and b can be set by themselves in combination with actual conditions. This embodiment does not make a rigid requirement. In this embodiment, a=128 and b=72 are taken as examples. Furthermore, the i-th nystagmus video frame may be compared with the previous frame or the previous frames. For example, a time range β is preset. The specific value of β can be set according to the actual situation. This embodiment does not limit this. In this embodiment, β=3 is used for description. All frames within β seconds before the current frame are recorded as comparison frames for use. The i-th nystagmus video frame and the i-1-th nystagmus video frame are divided into a number of eyeball local areas in the above manner.

[0058] Further, determining N compression results of each local area of ​​the eyeball at different compression ratios includes: performing downsampling processing on each local area of ​​the eyeball according to different compression ratios to obtain N compression results corresponding to each local area of ​​the eyeball.

[0059] Specifically, the pyramid downsampling method is used to construct a downsampling pyramid for each local area of ​​the eyeball, and any layer in the downsampling pyramid for each local area of ​​the eyeball is recorded as a compression layer. If there are N compression layers, N compression results are output. In this way, the pyramid downsampling method is used for each local area of ​​the eyeball, so that each local area of ​​the eyeball can output multiple compression results at one time.

[0060] In step S120, illustratively, according to the change in the number of pixels of M gray levels between each local eye area and each comparison local eye area, the important factors of the M gray levels in each local eye area are determined respectively, and the important factors of the M gray levels in each local eye area are used as the nystagmus characteristics contained in the M gray levels in each local eye area.

[0061] For example, since the important factors are only obtained through the dynamic changes of pixels between adjacent frames, they cannot accurately characterize the detailed features of nystagmus contained in the corresponding pixels of the grayscale. Nystagmus has the characteristic of stable nystagmus frequency, so the nystagmus factor of each grayscale can be obtained through the periodicity of the displacement of each grayscale corresponding pixel in each area within a certain time range, and the important factors of each grayscale are corrected, so as to accurately obtain the nystagmus features contained in the grayscale corresponding pixel.

[0062] Preferably, see Figure 2 , step S120 includes: S1210, according to the change of the number of pixels of M gray levels between each eyeball local area and each comparison eyeball local area, respectively determine the important factors of M gray levels in each eyeball local area;

[0063] S1220, using the preset M gray levels in each local area of ​​the eye to correct the important factors of the M gray levels in each local area of ​​the eye, respectively, to obtain the nystagmus characteristics contained in the M gray levels in each local area of ​​the eye.

[0064] Specifically, when the number of pixels corresponding to a certain gray level in the same area in adjacent frames changes greatly, it means that the pixels corresponding to the gray level are more likely to contain nystagmus details. Since the edges of the cornea and the iris contain a large number of detailed features, and the pixels corresponding to the cornea and the iris have a large difference in grayscale, the important factors of each gray level in each local area of ​​the eye can be obtained by combining the changes in the number of pixels corresponding to each gray level in the same area in adjacent frames.

[0065] Preferably, step S1210 includes performing regional extraction on M gray levels in each local eye area and each comparison local eye area, respectively, to obtain an eye sub-area corresponding to each local eye area and a comparison eye sub-area corresponding to each comparison local eye area; according to the change in the number of pixels of the M gray levels between the eye sub-area corresponding to each local eye area and the comparison eye sub-area corresponding to each comparison local eye area, respectively determine the important factors of the M gray levels in each local eye area.

[0066] Specifically, the jth grayscale level is extracted from the kth eyeball local area in the i-th nystagmus video frame to obtain the eyeball sub-area corresponding to the kth eyeball local area. The jth grayscale level is extracted from the kth comparison eyeball local area in the i-1th nystagmus video frame to obtain the comparison eyeball sub-area corresponding to the kth comparison eyeball local area. In this embodiment, any area in the i-th nystagmus video frame is recorded as the eyeball local area, and the area corresponding to the eyeball local area in the comparison frame is recorded as the comparison eyeball local area. Any grayscale level in the eyeball local area is recorded as the grayscale level to be measured (i.e., the jth grayscale level), and the part composed of the grayscale level to be measured in the eyeball local area is recorded as the eyeball sub-area. The part composed of the grayscale level to be measured in the comparison eyeball local area in a comparison frame is recorded as a comparison eyeball sub-area, and according to the acquisition time of the comparison frame corresponding to the comparison eyeball sub-area, all the comparison eyeball sub-areas are time-sequentially sorted according to the acquisition time of the corresponding frame to obtain a comparison eyeball sub-area sequence. It should be noted that the comparison frame used in step S120 is the i-1th nystagmus video frame, and the remaining comparison frames can be used in the remaining steps.

[0067] Furthermore, by combining the change in the number of pixels corresponding to each gray level in the same area in adjacent frames and the gradient value of the pixels corresponding to each gray level, the important factors of each gray level can be obtained more accurately. Preferably, step S1220 includes: determining the pixel change value according to the number of pixels of the jth gray level in the eyeball sub-area of ​​the kth eyeball local area and the number of pixels of the jth gray level in the contrast eyeball sub-area of ​​the kth contrast eyeball local area;

[0068] According to the pixel change value and the gradient mean of all pixel points corresponding to the jth gray level in the kth local area of ​​the eye, the important factor of the jth gray level in the kth local area of ​​the eye is determined; wherein k is a positive integer and j is a positive integer not greater than M.

[0069] In this embodiment, the calculation formula of the important factor of the jth gray level in the kth eyeball local area is as follows:

[0070]

[0071] in, represents the important factor of the jth gray level in the kth local area of ​​the eyeball, represents the area of ​​the contrast eyeball sub-region adjacent to the eyeball sub-region (i.e., the number of pixels in the contrast eyeball sub-region of the jth gray level in the kth contrast eyeball local region in the i-1th frame video); represents the area of ​​the eyeball sub-region (i.e., the number of pixels in the eyeball sub-region of the kth eyeball local area at the jth grayscale level in the i-th frame of the video); Indicates the eyeball sub-area The gradient value of each pixel; represents the absolute value function; represents the maximum value function; Represents a linear normalization function, whose normalization object is the corresponding grayscale of all gray levels in the local area of ​​the eyeball (That is, the gradient mean of all pixels corresponding to the jth gray level in the kth local area of ​​the eye).

[0072] The gradient value of the pixel point can be obtained by first obtaining the gradient values ​​in the horizontal and vertical directions according to the Sobel algorithm, and then obtaining the gradient values ​​in the horizontal and vertical directions. In this way, according to the above formula, for each local area of ​​the eyeball divided in the i-th frame of the nystagmus video frame, M gray levels are calculated in turn, and M important factors can be obtained for each local area of ​​the eyeball.

[0073] It should be noted that The larger the value of , the greater the change in the pixel corresponding to the gray level between the current frame and the previous frame. The pixel corresponding to the gray level is more likely to contain the detail features of nystagmus. The larger the value of is, the more likely the pixel corresponding to the gray level is to be located at the edge of the cornea and the iris, that is, the pixel corresponding to the gray level is more likely to contain the detailed features of nystagmus. The larger the value of , the greater the possibility that the pixel corresponding to the gray level contains nystagmus details. In step S130, for example, the important factors of the M gray levels in each local area of ​​the eyeball can be used as the nystagmus features contained in the M gray levels in the local area of ​​the eyeball. Then, after obtaining the nystagmus features contained in all gray levels in the local area of ​​the eyeball, the nystagmus detail loss degree of each compression result can be obtained according to the reduction amount of the pixel corresponding to each gray level in the N compression results, combined with the nystagmus features contained in each gray level.

[0074] Exemplarily, the method for determining the tremor factors of the preset M gray levels in each local eye area includes: when the kth local eye area of ​​the jth gray level is used for area extraction, the centroid distance between the eye sub-area of ​​the kth local eye area in the i-th frame nystagmus video frame and the contrast eye sub-area of ​​the kth contrast eye area in the nystagmus video frames between the i-1th frame and the itth frame is calculated to obtain the centroid distance corresponding to the nystagmus video frame; the centroid coordinates are generated according to the centroid distance corresponding to the nystagmus video frame corresponding to the time of the nystagmus video frame between the i-1th frame and the itth frame; the tremor factor of the jth gray level in the kth local eye area is determined according to the amplitude of the frequency spectrum corresponding to the centroid coordinates; wherein t is a positive integer less than i.

[0075] Specifically, the distance between the centroid of the eyeball sub-region of the current frame (i.e., the i-th nystagmus video frame) and the centroid of the comparison eyeball sub-region of the comparison frame (i.e., the nystagmus video frame between the i-1-th frame and the it-th frame) is calculated to obtain the centroid distance corresponding to each nystagmus video frame. Then, the time of the comparison frame is taken as the horizontal axis, and the corresponding centroid distance is taken as the vertical axis to form t+1 centroid coordinates. A rectangular coordinate system is constructed, and the t+1 centroid coordinates are placed in the rectangular coordinate system to obtain a scatter plot of the t+1 centroid coordinates, and all data points in the scatter plot are fitted to obtain a centroid distance curve for the i-th nystagmus video frame. Fourier transform is performed on the centroid distance for the i-th nystagmus video frame to obtain a frequency spectrum of the centroid distance curve for the i-th nystagmus video frame. The larger the amplitude of the frequency with the largest amplitude in the spectrum graph, the more concentrated the energy of the data at this frequency, that is, the stronger the periodicity of the data, and the stronger the periodicity, the more detailed features of nystagmus. Therefore, the maximum amplitude in the spectrum graph is used as the corresponding grayscale nystagmus factor.

[0076] Further, the nystagmus factor of the jth gray level in each local area of ​​the eyeball is multiplied by the important factor of the jth gray level in each local area of ​​the eyeball to obtain the nystagmus feature contained in the jth gray level in each local area of ​​the eyeball.

[0077] The calculation formula of the nystagmus feature contained in the jth gray level in each local area of ​​the eyeball is as follows:

[0078]

[0079] in, represents the nystagmus characteristics contained in each local area of ​​the eyeball at the jth gray level; represents the important factor of the jth gray level in the kth local area of ​​the eye; Represents the tremor factor of the jth gray level in the kth local area of ​​the eyeball.

[0080] In step S130, each compression layer in the downsampling pyramid is the compression result of the region at different compression ratios, and each compression result will discard a pixel point, and the nystagmus detail features contained in the pixels corresponding to different gray levels are different, so the nystagmus detail features contained in the discarded pixels can be used to count the number of pixels corresponding to each gray level in the region, and the number of pixels reduced in the pyramid layer, combined with the nystagmus features contained in each gray level, to obtain the nystagmus detail loss degree of each compression result.

[0081] Preferably, see Figure 3 , step S130 includes: S1310, calculating the ratio of the number of pixels of the j-th grayscale in the k-th local area of ​​the eyeball to the number of pixels of the j-th grayscale in the v-th compression result, and obtaining the pixel reduction parameter of the j-th grayscale in the v-th compression result; S1320, multiplying the pixel reduction parameter of the j-th grayscale in the v-th compression result with the nystagmus feature contained in the j-th grayscale in each local area of ​​the eyeball, and obtaining the sub-nystagmus detail loss degree of the j-th grayscale in the v-th compression result; S1330, summing the sub-nystagmus detail loss degrees of the M grayscales in the v-th compression result, and obtaining the nystagmus detail loss degree of the k-th local area of ​​the eyeball for the v-th compression result, wherein v is a positive integer not greater than N.

[0082] In this embodiment, the calculation formula of the tremor detail loss degree of the kth eyeball local area for the vth compression result is as follows:

[0083]

[0084] in, represents the tremor detail loss degree of the k-th eyeball local area for the v-th compression result; Indicates the number of pixels of the jth gray level in the vth compression result; represents the number of pixels of the jth gray level in the kth local area of ​​the eyeball; represents the nystagmus features contained in the jth gray level in each local area of ​​the eye, and M is the number of gray levels.

[0085] It should be noted that It represents the amount of reduction of the j-th gray level in the compression layer. When the j-th gray level is reduced more in the compression layer, and the pixel points corresponding to the j-th gray level contain more nystagmus detail features, the compression layer loses more nystagmus detail features. Therefore, when The larger the value is, the more nystagmus details are lost in the compression layer, which provides a basis for assigning compression ratios to each region later.

[0086] In step S140, illustratively, the more similar the eyeball local area is to the comparison eyeball local area, the more likely the eyeball local area is to be the background area, and there is no nystagmus detail feature in the background area, so the more similar the eyeball local area is to the comparison eyeball local area, the less important the eyeball local area is; at this time, the less nystagmus detail feature is present in the information lost by compressing the eyeball local area, and the nystagmus feature contained in each eyeball local area is characterized by the nystagmus coefficient of each eyeball local area. Therefore, it is necessary to use the nystagmus coefficient of each eyeball local area as a weight to correct the nystagmus detail loss degree of each compression result of the region; so that the compression layer can accurately represent the nystagmus detail loss degree.

[0087] Preferably, the method for determining the nystagmus coefficient of each local eye area includes: performing structural similarity calculations on the kth local eye area in the i-th nystagmus video frame and the kth comparative local eye area in the nystagmus video frames between the i-1-th frame and the it-th frame, respectively, to obtain t+1 structural similarity parameters; and determining the nystagmus coefficient of the kth local eye area according to the t+1 structural similarity parameters.

[0088] Specifically, the structural similarity (Structural Similarity Index) between the local eye region and each comparative local eye region is obtained. Since the calculation process of the structural similarity is a well-known prior art, it will not be described in detail in this embodiment. The mean of the structural similarity parameters between the local eye region and each comparative local eye region is calculated, and the normalized result of the negative correlation of the mean is used as the nystagmus coefficient of the local eye region. It should be noted that the smaller the nystagmus coefficient value, the fewer nystagmus detail features contained in the local eye region, and the more likely it is to be a background region, and the background region can accept greater losses without affecting the detail features of the nystagmus in the video.

[0089] In this embodiment, after the nystagmus coefficient of the k-th local area of ​​the eyeball is determined, the nystagmus coefficient of the k-th local area of ​​the eyeball is multiplied by the nystagmus detail loss degree of the k-th local area of ​​the eyeball for the v-th compression result to obtain the nystagmus detail loss degree of the k-th local area of ​​the eyeball for the v-th compression result. The specific formula is as follows:

[0090]

[0091] in, represents the nystagmus detail loss degree of the k-th eyeball local area for the v-th compression result; represents the tremor detail loss degree of the k-th eyeball local area for the v-th compression result; Represents the nystagmus coefficient of the kth local area of ​​the eye.

[0092] In this way, the nystagmus detail loss degree is calculated for each compression result of each local area of ​​the eyeball according to the above formula, and N nystagmus detail loss degrees for each local area of ​​the eyeball for the N compression results are obtained.

[0093] In step S150, the preset loss degree threshold is exemplarily set based on experience and is not limited here. In this embodiment, the preset loss degree threshold is set to 0.8.

[0094] Preferably, the compression result with less than a preset loss degree threshold among N nystagmus detail loss degrees is determined as the target compression result for each local area of ​​the eyeball, including: determining the compression result with less than a preset loss degree threshold among N nystagmus detail loss degrees as the alternative compression result; and selecting the alternative compression result with the highest compression ratio among the alternative compression results as the target compression result for each local area of ​​the eyeball.

[0095] Specifically, the N nystagmus detail loss degrees of the kth eyeball local area for N compression results are compared with 0.8 respectively, and the nystagmus detail loss degrees less than 0.8 are screened out. The compression result corresponding to the nystagmus detail loss degree less than 0.8 is used as the candidate compression result of the kth eyeball local area, and the candidate compression result with the highest compression ratio is selected from the candidate compression results as the target compression result of the kth eyeball local area. In this way, all the eyeball local areas divided from the i-th nystagmus video frame are processed according to the above calculation method to obtain the target compression result of each eyeball local area. Finally, the i-th nystagmus video frame is transmitted according to the target compression results of each eyeball local area. The compressed video frame can retain the nystagmus details. In this way, a small compression ratio is given to the area with nystagmus details to retain its detail features, and a large compression ratio is given to the area without nystagmus details to increase the transmission speed.

[0096] In the technical solution of the present application, the i-th frame of nystagmus video frame and the i-1-th frame of nystagmus video frame are divided respectively to obtain each local eye region of the i-th frame of nystagmus video frame and each comparative local eye region of the i-1-th frame of nystagmus video frame, and N compression results of each local eye region under different compression ratios are determined; at least the change in the number of pixels of M gray levels between each local eye region and each comparative local eye region is used to determine the nystagmus features contained in the M gray levels in each local eye region; according to the nystagmus features contained in the M gray levels in each local eye region, the M gray levels are determined. The number of pixels of the gray level in the N compression results and the number of pixels of the M gray level in each local area of ​​the eyeball are used to determine the N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results; the corresponding N nystagmus detail loss degrees are respectively corrected by using the nystagmus coefficient of each local area of ​​the eyeball to obtain the N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results; the compression result of the N nystagmus detail loss degrees less than the preset loss degree threshold is determined as the target compression result of each local area of ​​the eyeball, and the i-th nystagmus video frame is transmitted according to the target compression result of each local area of ​​the eyeball. In this way, by determining the nystagmus detail loss degrees of each local area of ​​the eyeball for different compression results, each local area of ​​the eyeball selects its own compression result, avoiding the whole frame data from being compressed with the same compression ratio, and each local area of ​​the eyeball can select a compression result that retains the detail features of the nystagmus, ensuring the integrity of the information in the compression result, thereby effectively transmitting the nystagmus video frame.

[0097] See also Figure 4 , which shows a schematic diagram of a remote video transmission system for nystagmus of a vertigo patient provided by an embodiment of the present invention. In an exemplary embodiment, a remote video transmission system for nystagmus of a vertigo patient is provided, comprising:

[0098] The dividing module 410 is used to divide the i-th nystagmus video frame and the i-1-th nystagmus video frame respectively, obtain each eyeball local area of ​​the i-th nystagmus video frame and each comparison eyeball local area of ​​the i-1-th nystagmus video frame, and determine N compression results of each eyeball local area under different compression ratios;

[0099] The first processing module 420 is used to determine the nystagmus features of the M gray levels in each local area of ​​the eyeball by at least using the change in the number of pixels of the M gray levels between each local area of ​​the eyeball and each comparative local area of ​​the eyeball;

[0100] The second processing module 430 is used to determine N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results according to the nystagmus features contained in the M gray levels in each local area of ​​the eyeball, the number of pixels of the M gray levels in the N compression results, and the number of pixels of the M gray levels in each local area of ​​the eyeball;

[0101] The third processing module 440 is used to respectively correct the corresponding N nystagmus detail loss degrees using the nystagmus coefficient of each local area of ​​the eyeball, and obtain N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results;

[0102] The transmission module 450 is used to determine the compression results of N nystagmus detail loss degrees that are less than a preset loss degree threshold as the target compression results of each local area of ​​the eyeball, and transmit the i-th frame of nystagmus video frame according to the target compression results of each local area of ​​the eyeball; wherein M, N and i are positive integers.

[0103] The device for remote transmission of video of nystagmus of a patient with vertigo provided in this embodiment belongs to the same application concept as the method for remote transmission of video of nystagmus of a patient with vertigo provided in the above embodiment of this application, and can execute the method for remote transmission of video of nystagmus of a patient with vertigo provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of executing the method for remote transmission of video of nystagmus of a patient with vertigo. For the technical details not described in detail in this embodiment, please refer to the specific processing content of the method for remote transmission of video of nystagmus of a patient with vertigo provided in the above embodiment of this application, which will not be repeated here.

[0104] In other embodiments, a device is also provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the above-mentioned method for remote transmission of nystagmus video of a vertigo patient. The device can be specifically a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the above-mentioned method for remote transmission of nystagmus video of a vertigo patient provided in the above-mentioned embodiment.

[0105] In other embodiments, a computer program product is also provided. When the computer program product is run on a computer, the computer is caused to execute the above-mentioned related steps to implement a method for remote transmission of nystagmus video of a vertigo patient provided in the above-mentioned embodiment.

[0106] In other embodiments, a computer-readable storage medium is also provided, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement a method for remote transmission of nystagmus video of a vertigo patient provided in the above-mentioned embodiment.

[0107] Among them, the provided systems, devices, computer program products, and computer-readable storage media are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above and will not be repeated here.

[0108] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0109] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for remotely transmitting video of nystagmus of vertigo patients, characterized in that: The method comprises: The i-th nystagmus video frame and the i-1-th nystagmus video frame are divided respectively to obtain each eyeball local area of ​​the i-th nystagmus video frame and each comparison eyeball local area of ​​the i-1-th nystagmus video frame, and N compression results of each eyeball local area under different compression ratios are determined; At least using the change in the number of pixels of M gray levels between each eyeball local area and each comparison eyeball local area, determine the nystagmus features of the M gray levels in each eyeball local area respectively; Determine N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results according to the nystagmus features contained in the M gray levels in each local area of ​​the eyeball, the number of pixels of the M gray levels in the N compression results, and the number of pixels of the M gray levels in each local area of ​​the eyeball; Using the nystagmus coefficient of each local area of ​​the eyeball, respectively correct the corresponding N nystagmus detail loss degrees, and obtain N nystagmus detail loss degrees of each local area of ​​the eyeball for N compression results; The compression results with N nystagmus detail loss degrees less than a preset loss degree threshold are determined as target compression results of each local eye area, and the i-th nystagmus video frame is transmitted according to the target compression results of each local eye area; wherein M, N and i are positive integers.

2. A method for remotely transmitting video of nystagmus of a vertigo patient according to claim 1, characterized in that: At least according to the change of the number of pixels of M gray levels between each local area of ​​the eyeball and each comparative local area of ​​the eyeball, the nystagmus features of the M gray levels in each local area of ​​the eyeball are determined respectively, including: According to the change of the number of pixels of M gray levels between each local area of ​​the eyeball and each comparative local area of ​​the eyeball, respectively determine the important factors of the M gray levels in each local area of ​​the eyeball; By using the preset nystagmus factors of the M gray levels in each local area of ​​the eyeball, the important factors of the M gray levels in each local area of ​​the eyeball are corrected respectively to obtain the nystagmus characteristics contained in the M gray levels in each local area of ​​the eyeball.

3. A method for remotely transmitting video of nystagmus of a vertigo patient according to claim 2, characterized in that: According to the change in the number of pixels of the M gray levels between each local area of ​​the eyeball and each comparative local area of ​​the eyeball, important factors of the M gray levels in each local area of ​​the eyeball are determined respectively, including: Performing region extraction on M gray levels in each eyeball local region and each comparison eyeball local region respectively, to obtain an eyeball sub-region corresponding to each eyeball local region and a comparison eyeball sub-region corresponding to each comparison eyeball local region; According to the change in the number of pixels of M gray levels between the eyeball sub-areas corresponding to each eyeball local area and the contrasting eyeball sub-areas corresponding to each contrasting eyeball local area, the important factors of the M gray levels in each eyeball local area are determined respectively.

4. A method for remotely transmitting video of nystagmus of a vertigo patient according to claim 3, characterized in that: According to the change in the number of M grayscale pixels between the eyeball sub-regions corresponding to each eyeball local region and the contrast eyeball sub-regions corresponding to each contrast eyeball local region, important factors of the M grayscales in each eyeball local region are determined respectively, including: Determine a pixel change value according to the number of pixels of the jth gray level in the eyeball sub-region of the kth eyeball local region and the number of pixels of the jth gray level in the contrast eyeball sub-region of the kth contrast eyeball local region; According to the pixel change value and the gradient mean of all pixel points corresponding to the jth gray level in the kth local area of ​​the eye, the important factor of the jth gray level in the kth local area of ​​the eye is determined; wherein k is a positive integer and j is a positive integer not greater than M.

5. A method for remote transmission of nystagmus video of vertigo patients according to claim 4, characterized in that: The method for determining the tremor factor of the preset M gray levels in each local area of ​​the eyeball includes: In the case of using the kth eyeball local area of ​​the jth grayscale level for region extraction, respectively calculating the centroid distance between the eyeball sub-area of ​​the kth eyeball local area in the i-th nystagmus video frame and the contrast eyeball sub-area of ​​the kth contrast eyeball local area in the nystagmus video frames between the i-1th frame and the itth frame, to obtain the centroid distance corresponding to the nystagmus video frame; Generate centroid coordinates according to centroid distances corresponding to nystagmus video frames corresponding to times of nystagmus video frames between the i-1th frame and the itth frame; The tremor factor of the jth gray level in the kth local area of ​​the eyeball is determined according to the amplitude of the frequency spectrum corresponding to the centroid coordinates; wherein t is a positive integer less than i.

6. A method for remotely transmitting video of nystagmus of a vertigo patient according to claim 2, characterized in that: According to the nystagmus features contained in the M gray levels in each local area of ​​the eyeball, the number of pixels of the M gray levels in the N compression results, and the number of pixels of the M gray levels in each local area of ​​the eyeball, N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results are determined, including: Calculate the ratio of the number of pixels of the j-th gray level in the k-th local area of ​​the eyeball to the number of pixels of the j-th gray level in the v-th compression result, and obtain the pixel reduction parameter of the j-th gray level in the v-th compression result; Multiplying the pixel reduction parameter of the j-th gray level in the v-th compression result by the nystagmus feature contained in each local area of ​​the eyeball of the j-th gray level, to obtain the sub-nystagmus detail loss degree of the j-th gray level in the v-th compression result; The sub-tremor detail loss degrees of the M gray levels in the v-th compression result are summed to obtain the tremor detail loss degree of the k-th local area of ​​the eyeball for the v-th compression result, where v is a positive integer not greater than N.

7. The method for remotely transmitting video of nystagmus of vertigo patients according to claim 1, characterized in that: Determine N compression results of each local area of ​​the eyeball at different compression ratios, including: Downsampling is performed on each local area of ​​the eyeball according to different compression ratios to obtain N compression results corresponding to each local area of ​​the eyeball.

8. The method for remotely transmitting video of nystagmus of vertigo patients according to claim 1, characterized in that: Methods for determining the nystagmus coefficient of each local area of ​​the eyeball include: Structural similarity calculations are performed on the kth eyeball local region in the i-th nystagmus video frame and the kth comparative eyeball local region in the nystagmus video frames between the i-1-th frame and the it-th frame, respectively, to obtain t+1 structural similarity parameters; The nystagmus coefficient of the kth local area of ​​the eyeball is determined according to the t+1 structural similarity parameters.

9. A method for remotely transmitting video of nystagmus of a vertigo patient according to any one of claims 1 to 8, characterized in that: The compression results with N nystagmus detail loss degrees less than a preset loss degree threshold are determined as target compression results of each eyeball local area, including: The compression results with N nystagmus detail loss degrees less than a preset loss degree threshold are determined as candidate compression results; and the candidate compression result with the highest compression ratio is selected from the candidate compression results as the target compression result of each local area of ​​the eyeball.

10. A remote video transmission system for nystagmus of vertigo patients, characterized in that: include: A division module is used to divide the i-th nystagmus video frame and the i-1-th nystagmus video frame respectively, obtain each eyeball local area of ​​the i-th nystagmus video frame and each comparison eyeball local area of ​​the i-1-th nystagmus video frame, and determine N compression results of each eyeball local area under different compression ratios; A first processing module is used to determine the nystagmus features of the M gray levels in each local area of ​​the eyeball by at least using the change in the number of pixels of the M gray levels between each local area of ​​the eyeball and each comparative local area of ​​the eyeball; The second processing module is used to determine N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results according to the nystagmus features contained in the M gray levels in each local area of ​​the eyeball, the number of pixels of the M gray levels in the N compression results, and the number of pixels of the M gray levels in each local area of ​​the eyeball; The third processing module is used to respectively correct the corresponding N nystagmus detail loss degrees using the nystagmus coefficient of each local area of ​​the eyeball, and obtain N nystagmus detail loss degrees of each local area of ​​the eyeball for the N compression results; The transmission module is used to determine the compression results of N nystagmus detail loss degrees that are less than a preset loss degree threshold as the target compression results of each local area of ​​the eyeball, and transmit the i-th frame of nystagmus video frame according to the target compression results of each local area of ​​the eyeball; wherein M, N and i are positive integers.

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