CN116962708B - Intelligent service cloud terminal data optimization transmission method and system - Google Patents
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Abstract
The invention relates to the technical field of image communication processing, in particular to an intelligent service cloud terminal data optimization transmission method and system for acquiring video data; acquiring data exploration coefficients of each data sub-block of the reference frame; obtaining the characteristic sequence of each search step length of each search window according to the data exploration coefficient sequence of each search step length of each search window of the current frame and the data exploration coefficient of each data sub-block of the reference frame; obtaining the data exploration influence coefficients of each search window according to the characteristic sequences of each search step length of each search window of the current frame and the data exploration coefficients of each data sub-block of the reference frame; obtaining the exploration gain step length of each search window according to the data exploration influence coefficient of each search window of the current frame; and calculating motion compensation between adjacent frames of the TTS algorithm according to the exploration gain step length of each search window of the current frame, and completing compression transmission of the video. The efficiency and quality of video data compression are improved, registration errors are reduced, and the transmission of cloud terminal data is optimized.
Description
Technical Field
The invention relates to the technical field of image communication processing, in particular to an intelligent service cloud terminal data optimization transmission method and system.
Background
The arrival of the information age and the big data age enables the cloud computing technology to be rapidly developed, and the cloud computing technology is used as a novel network application mode and is mainly used for providing high-efficiency services such as data storage, data sharing and computing aiming at different user demands. Along with the intelligent terminal bringing more convenience to people, more defects exist, such as insufficient storage capacity, information loss in the cloud computing data transmission process, optimization problem of transmission efficiency and the like.
Therefore, optimization of the data transmission process of the intelligent service cloud terminal is a problem to be solved. In the data transmission process, the compressed data can accelerate the data transmission efficiency. For intelligent service cloud terminal data, compression transmission of video is an important aspect, and for some video with small information quantity change among frames, a three-step search algorithm is generally used to compress content with the same data, namely a process of extracting video sequence motion trend from a current frame.
When the traditional three-step search algorithm performs compression processing of the same data content among video frames, aiming at video data which has no more prominent features and has large-area repeated content, the compression processing error is larger and the efficiency is lower, so that distortion phenomenon occurs in the video compression process, and information loss in the data transmission process of the intelligent cloud terminal is caused.
In summary, the invention provides an intelligent service cloud terminal data optimization transmission method and system, which are used for acquiring video data, analyzing the data characteristics of each adjacent frame image in the video data, and obtaining the self-adaptive initial search step length of each data sub-block, so that the matching result of different data sub-blocks in each adjacent frame is more accurate, the compression of the video data is completed, and the transmission process of the data is optimized.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent service cloud terminal data optimization transmission method and system, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides an intelligent service cloud terminal data optimization transmission method, where the method includes the following steps:
acquiring video data of an intelligent service cloud terminal;
partitioning each frame of image of video data to obtain each data sub-block; acquiring data exploration coefficients of each data sub-block of the reference frame; determining the search step length and the search window of each data sub-block of the current frame; acquiring data exploration coefficients of each data sub-block and each search window of the current frame;
for each search window of the current frame, the data exploration coefficients of each search step neighborhood data sub-block of the search window are combined into a data exploration coefficient sequence of each search step of the search window; obtaining a characteristic sequence of each search step length of the search window according to the data exploration coefficient sequence of each search step length of the search window and the data exploration coefficient of the data sub-block corresponding to the reference frame; obtaining the data exploration influence coefficient of the search window according to the characteristic sequence of each search step length of the search window and the data exploration coefficient of the data sub-block corresponding to the reference frame; converting the feature sequence of each search step length of the search window into a decimal exploration selection feature value; obtaining the exploration gain step length of the search window according to the data exploration influence coefficient of the search window and the exploration selection characteristic value of each search step length;
calculating motion compensation between adjacent frames of the TTS algorithm according to the exploration gain step length of the search window; and compressing and transmitting the video according to the motion compensation of each adjacent frame.
Preferably, the acquiring the data exploration coefficient of each data sub-block of the reference frame includes:
for each data sub-block of the reference frame, acquiring row characteristic differences and column characteristic differences of the data sub-blocks;
and taking the average value of the row characteristic differences and the column characteristic differences as a data exploration coefficient of the data sub-block.
Preferably, the acquiring the row characteristic difference and the column characteristic difference of the data sub-block includes:
for each data sub-block of the reference frame, calculating the DTW distance of each adjacent data sequence of the data sub-block; taking the sum of the DTW distances of all adjacent data sequences of the data sub-block as the line characteristic difference of the data sub-block;
calculating the DTW distance of each adjacent column of data sequence of the data sub-block; and taking the sum of the DTW distances of all adjacent columns of data sequences of the data sub-block as the column characteristic difference of the data sub-block.
Preferably, the determining the search step length and the search window of each data sub-block of the current frame includes:
setting the searching step length of each data sub-block of the current frame;
and taking the data sub-blocks of the current frame as the center, and taking the range which is respectively included by the data sub-blocks with four searching step lengths from top to bottom, left to right as the searching window of the data sub-blocks of the current frame.
Preferably, the obtaining the feature sequence of each search step of the search window according to the data exploration coefficient sequence of each search step of the search window and the data exploration coefficient of the corresponding data sub-block of the reference frame includes:
for each search window of the current frame, taking the data exploration coefficient of the data sub-block corresponding to the reference frame as a distribution threshold value of the search window;
for the data exploration coefficient sequences of each search step length of a search window, marking the distribution coefficient of the data subblocks with the numerical value larger than the distribution threshold value in the data exploration coefficient sequences as 1; marking the distribution coefficient of the data subblocks with the numerical value smaller than the distribution threshold value in the data exploration coefficient sequence as 0;
and taking the distribution coefficient of each data sub-block of each search step of the search window as the element of the characteristic sequence of each search step of the search window.
Preferably, the obtaining the data exploration influence coefficient of the search window according to the feature sequence of each search step length of the search window and the data exploration coefficient of the data sub-block corresponding to the reference frame includes:
for each search window of the current frame, obtaining the edge coefficient of the search window according to the search window and the data exploration coefficient of the corresponding data sub-block of the reference frame; obtaining the difference coefficient of the search window according to the characteristic sequence of each search step length of the search window;
and taking the product of the edge coefficient and the difference coefficient as a data exploration influence coefficient of a search window.
Preferably, the obtaining the edge coefficient of the search window according to the search window and the data exploration coefficient of the data sub-block corresponding to the reference frame includes:
for each search window of the current frame, calculating the absolute value of the difference value of the data exploration coefficients of the corresponding data sub-blocks of the search window and the reference frame, and taking the exponential function value of the absolute value of the difference value as the edge coefficient of the search window.
Preferably, the obtaining the difference coefficient of the search window according to the feature sequence of each search step of the search window includes:
for each search window of the current frame, calculating the Hamming distance between each adjacent search step feature sequence of the search window; and taking the Hamming distance average value of all adjacent search steps of the search window as the difference coefficient of the search window.
Preferably, the obtaining the exploration gain step length of the search window according to the data exploration influence coefficient of the search window and the exploration selection feature value of each search step length includes:
when the data exploration influence coefficient of the search window is larger than a judgment threshold value, setting the exploration gain step length of the search window as T, wherein T is the set initial search step length;
and when the data exploration influence coefficient of the search window is smaller than the judging threshold value, setting the exploration gain step length of the search window as the search step length of the minimum exploration selection characteristic value.
In a second aspect, an embodiment of the present invention further provides an intelligent service cloud terminal data optimization transmission system, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The embodiment of the invention has at least the following beneficial effects:
the invention relates to an intelligent service cloud terminal data optimization transmission method and system, which are characterized in that the data distribution characteristics of each data sub-block of a reference frame are analyzed to construct the data exploration coefficients of each data sub-block, so as to represent the data difference between each data sub-block row and each data sub-block column, and the obvious degree of the data characteristics of each data sub-block is reflected on the side surface, thereby being convenient for the accurate identification of the subsequent matching process and reducing the registration error;
the method has the advantages that the distribution characteristics of the data in each search window of the corresponding current frame in the reference frame are analyzed, the data sub-blocks of each search step length in the search window obtained through coarse registration are considered to be gradually expanded outwards, namely the complexity of the data information of the moving object in the video data is considered, the exploration influence coefficient of each search window is calculated, whether the search range is increased when registration is carried out is determined through the exploration influence coefficient, the change of the data information of the area far away from the edge of the object is small, the optimal motion estimation area is determined by adding the search area, and the motion compensation of the same data is calculated more accurately; in the mode, each frame of image is not required to be encoded so as to cause redundancy in time and space, the motion trend and trend in the current frame are only required to be transmitted to the decoder, the decoder acquires the current frame of image according to the content of the previous frame and the motion trend of the current frame, the time consumed by video in the transmission process is effectively reduced, the efficiency and quality of video data compression in intelligent service cloud terminal data are improved, and the transmission of the cloud terminal data is optimized.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of steps of an intelligent service cloud terminal data optimization transmission method according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the intelligent service cloud terminal data optimization transmission method and system according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The following specifically describes a specific scheme of the intelligent service cloud terminal data optimization transmission method and system provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of an intelligent service cloud terminal data optimization transmission method according to an embodiment of the present invention is shown, where the method includes the following steps:
and S001, acquiring video data of the intelligent service cloud terminal and preprocessing the video data.
According to the embodiment, the transmission mode of intelligent service cloud terminal data is optimized through an image communication processing technology, a large amount of different types of data are stored in the intelligent service cloud terminal data, and the equipment for storing the data comprises a sensor, a metering instrument, video monitoring and the like; the data collected by the equipment comprises various parameter data such as ambient temperature, humidity, air pressure, illumination and the like, and multimedia data such as video, audio and the like; and user data, system data, external data, etc.
The video data stored by the intelligent service cloud terminal generally occupies a larger space, and is easy to be interfered by noise and other external factors when the video data is compressed and stored, so that the quality of video compression transmission is reduced.
In this embodiment, the obtained video data is preprocessed by a data cleaning algorithm, and the implementation process of the data cleaning algorithm is a known technology and will not be described here.
So far, the video data of the cloud terminal can be obtained according to the method.
Step S002, the motion compensation of each adjacent frame is calculated by three-step search algorithm for the obtained video data.
For the transmission of intelligent service cloud terminal data, the data compression mode can be adopted for optimized transmission. And for the transmission of video data in cloud terminal data, compression transmission can be performed in a motion estimation mode.
The motion estimation is a process of extracting the motion trend and trend of the video sequence in the current frame by contrast with the reference frame, and the current frame can be restored based on the reference frame image through the motion estimation, so that the compression efficiency is improved. Wherein the reference frame is an image of a neighboring frame before the current frame.
Through the steps, the cloud terminal storage video data can be obtained. The present embodiment is directed to compression transmission of video in which a moving object exists.
The difference between each frame of images in the video is caused by the difference of the data information of the moving object in the images, and the data of the image information except the moving object are approximately the same, so that the same content and data in adjacent frames can be compressed, and the transmission efficiency of the video data is improved.
In the embodiment, a motion estimation algorithm based on TSS is adopted for processing, and the search step length is optimized before the matching process of the data sub-blocks to obtain more accurate motion compensation aiming at the data information difference between the current frame and the reference frame, namely the characteristics of the moving object and the characteristics of different registration areas.
Since the size of each frame of image is the same in one video data, when motion estimation is performed based on the three-step method, each frame of image in the video can be segmented in the same manner, and the size window of the segmentation is 5*5, so that each data sub-block of each frame of image is obtained.
Any two adjacent frames of image data in the video data are selected for analysis, and most of data information is the same because the interval time between the adjacent frames of images is shorter. When matching is considered between the data sub-blocks of two adjacent frames of images in the video data, the position of each data sub-block can be determined according to an initial coarse matching process.
In order to acquire the motion trend of the current frame image relative to the reference frame image, a corresponding sub-block needs to be found in the current frame for any one data sub-block in the reference frame. The present embodiment analyzes the ith data sub-block of the reference frame image.
Since the interval time between two adjacent frames of images is short, the moving position of the moving object in a short time is also small. In order to identify the moving position of the ith data sub-block of the reference frame in the current frame image, it is necessary to analyze the data characteristics around the position of the ith data sub-block of the reference frame image.
The more severe the change in data in a data sub-block surrounding a corresponding location, the more pronounced the change in characteristics surrounding that data sub-block, which is more readily localized to the location of a data sub-block of the current frame that is similar to the reference frame.
In the method, in the process of the invention,、respectively representing the ith data sub-block of the reference frameLine 1The data sequence of the rows,、respectively representing the ith data sub-block of the reference frameColumn (th)The data sequence of the columns is such that,for the window size of each data sub-block of the reference frame, in this embodimentThe operator can set the setting by himself,as a function of the distance of the DTW,exploring coefficients for data of the ith data sub-block of the reference frame, wherein,for the line feature differences of the ith data sub-block of the reference frame,the column characteristic difference for the ith data sub-block of the reference frame.
It should be noted that the DTW distance is a known technique, and this embodiment is not described in detail. If the change between the row and column data in the data sub-block is larger, then the calculation is performedAndthe larger the obtained data exploration coefficientThe larger the value of (c) is, the more obvious the data characteristic in the data sub-block is, namely, the motion compensation calculation is easier to be performed when the data sub-block in the current frame similar to the ith data sub-block of the reference frame is searched, so that the video compression coding precision is improved.
Through the analysis, the reference frame is obtainedThe data exploration coefficients of the data sub-blocks further analyze the size of a search window based on TSS algorithm matching motion estimation for the data sub-blocks at the corresponding positions of the current frame.
In consideration of small change of data information of a moving object between each frame of images in a video, an initial search step T of a data sub-block corresponding to a current frame is set to 4, and distances from left and right to a center data sub-block are respectively 4 sub-blocks, so that a search window has a size of 9*9.
For each search window, the present embodiment improves the initial search step of the algorithm by analyzing the feature differences exhibited by the data sub-blocks for each search step by analyzing the first step, the second step, the third step, and the fourth step, respectively.
Each step length has eight neighborhood data sub-blocks as characteristic data sub-blocks of the step length, namely, each step length of each distance center data sub-block has eight neighborhood data sub-blocks in eight directions to characterize the step length.
Repeating the method, and calculating the data exploration coefficients of each data sub-block and each search window of the current frame.
At the current frameIn the search window with the data subblocks as the center, the data exploration coefficient of the search window is calculated. For each searching step length, the data exploration coefficients of eight neighborhood data sub-blocks of the searching step length are formed into a sequence. Searching the data of each searching step length of the ith searching window of the current frame to search the coefficient sequenceEach value of the data is compared with the data exploration coefficient of the ith data sub-block of the reference frame respectively, namely the data exploration coefficient of the ith data sub-block of the reference frameSetting a distribution threshold value of the ith search window of the current frame, and counting the distribution condition of each data sub-block in the data exploration coefficient sequence of each search step length of the ith search window of the current frame.
In the method, in the process of the invention,representing the current frameSearch windowsSearching the data exploration coefficient of the eight neighborhood data sub-block a with the step length of t,exploring coefficients for the data of the ith data sub-block of the reference frame,representing the current frameThe eight-neighborhood data sub-block a with a step size of t is searched for distribution coefficients by the search window.
It should be noted that, the data exploration coefficient of the ith data sub-block of the reference frameJudging the distribution threshold value of the ith search window of the current frame, setting the distribution coefficient of the data subblocks with the data exploration coefficients larger than the distribution threshold value and smaller than the distribution threshold value in each search step length of the ith search window of the current frame as 1, and setting the distribution coefficient of the data subblocks with the data subblocks smaller than the distribution threshold value as 0, so that the characteristic sequence of each search step length of the ith data subblock of the current frame can be obtained according to the distribution coefficient of the eight-neighborhood data subblocks of each search step length of the ith data subblock of the current frame. The sequence being obtained by the above calculation in the form of only 1 and 0, e.g。
The smaller the registration error of the same data in the video data compression process is, the better the quality of video compression is.
Therefore, by obtaining the characteristic sequence of each search step eight neighborhood data sub-block of each search window of the current frame, the data characteristic of the search step corresponding to the data sub-block to be registered in the reference frame can be represented.
The characteristic sequences of each search step length of each search window can be obtained through the calculation, each characteristic sequence is generated by comparing the data exploration coefficient of each search step length eight-neighborhood data sub-block of the current frame search window with the data exploration coefficient of each data sub-block corresponding to the reference frame, therefore, the characteristic sequences are shown to be based on the data characteristics of the data sub-blocks of the reference frame, the exploration comparison is carried out step by step outwards at the position of the corresponding search window of the current frame, and further, the characteristic change of the eight-neighborhood data sub-blocks relative to the central area is shown. The purpose of exploration with gradual expansion is to further analyze the position of the reference frame data sub-block in the current frame.
For any data sub-block, the closer to the edge of the object, the more obvious the change of the data information is, the faster the position change of the motion can be determined, and the motion compensation calculation can be more accurately carried out.
For the obtained characteristic sequences of each search step length in each search window of the current frame, in order to represent the change condition of the characteristic sequences of each search step length when each search step length is outwards extended in each search window of the current frame corresponding to each data sub-block of the reference frame, namely judging whether the situation of more intense data change occurs in the extending process or not, so as to represent the data characteristics of each data sub-block in the search window to be more prominent and obvious, and the identification degree is high, namely determining the motion position change of the corresponding data sub-block more quickly and accurately; and then, combining the similarity degree of the data characteristics of the whole search window of each data sub-block of the reference frame and the corresponding position of the current frame, and jointly representing the data exploration influence coefficients of each search window.
With reference frame numberFor example, the data sub-block calculates the data exploration influence coefficient of the ith search window of the current frame.
In the method, in the process of the invention,、respectively representing the ith search window search step length of the current frame、Is characterized by the fact that,representing reference frame numberThe data of the sub-blocks of data explore the coefficients,representing the current frameThe data of the search window explores the coefficients,as a function of the hamming distance,as an exponential function based on a natural constant e,as a function of the linear normalization,exploring the influence coefficient for the data of the ith search window of the current frame, wherein,for the edge coefficient of the i-th search window of the current frame,and the difference coefficient of the ith search window of the current frame.
If the search window is closer to the edge of the object in the image, the search window is calculatedThe larger the value of (2) is, the larger the data characteristic difference between the data sub-block in the reference frame and the corresponding search window of the current frame is, which indicates that the lower the possibility of error occurrence when similar data sub-blocks are searched, namely, the easier accurate positioning is realized during positioning; meanwhile, in the process of expanding the searching step by step outwards, the more violent the data change is, the more the data is calculatedThe larger the value of (2), the obtained data explores the influence coefficientThe larger the value of (2) is, the more regular and similar outwards-expanded data content does not exist in the search area, and the best matching data sub-block of motion estimation can be more efficiently found; calculated and obtainedThe smaller the value of (2) is, the more similar the sub-blocks of data to be matched are in the search window, the larger the motion compensation error calculated during positioning is, the video compression coding may cause distortion, and the video compression quality is poor.
The present embodiment considers that the closer to the edge region of the object is calculatedIs gradually increased, i.e. the more pronounced the featuresThe larger.
Setting a judgment thresholdIf the threshold value is smaller, the search area is not close to the edge of the object, and if the threshold value is larger, the search area is close to the edge of the object. Wherein, the value of the judgment threshold can be set by the implementer.
Through the analysis, based on the obtained data exploration influence coefficients of each search window, adding search data sub-blocks to the search window with larger calculation error, namely setting two initial search steps, and searching 16 data sub-blocks in total; and taking the search window with smaller calculation error as the initial search step length of the search window according to the search step length of 4 of the original algorithm, and searching 8 data sub-blocks in total.
Therefore, the characteristic sequences of each search step length of each search window obtained by the above method can be further filteredConverting binary numbers of each sequence into decimal numbers to obtain exploration selection characteristic valuesThe search gain step size of each search window is calculated from this.
In the method, in the process of the invention,for a value range of [1,3 ]]Is used for the search step of (a),as a function of the minimum value of the function,selecting a characteristic value for the exploration of the ith search window search step length t of the current frame,exploring the influence coefficient for the data of the ith search window of the current frame,in order to determine the threshold value,the gain step is explored for the ith search window of the current frame.
It should be noted that the initial decision is added based on the algorithm based on TSS motion estimationWhen breakingGreater than the judgment thresholdThe data features of the search window are obvious, and similar target data sub-blocks are easy to identify, so that eight search data sub-blocks are searched according to the original algorithm, namely, calculation is performed according to the initial search step length of 4; when (when)Less than the judgment thresholdThe data features in the search window are relatively flat and have no prominent features, errors are relatively easy to occur when similar target data sub-blocks are searched, then eight search gain step eight neighborhood data sub-blocks with minimum search selection feature values are added on the basis of eight search data sub-blocks of the original algorithm, and 16 search data sub-blocks are searched, namely, the search step is carried out according to the basis that the initial search step is 4The initial searching step length of the window is also used for increasing the searching range and reducing the error.
So far, the motion compensation between adjacent frames of the video data to be compressed can be more accurately obtained through the calculation.
Step S003, inter-frame prediction encoding is performed according to the motion compensation of each adjacent frame, and video is compressed according to the encoding result.
The motion compensation of all adjacent frames in the video data can be obtained through the calculation, and the flow of compressing and transmitting the video through the motion compensation in the embodiment is as follows:
1. acquiring corresponding motion compensation through video data; namely, according to the motion compensation of adjacent frames, the spatial position corresponding relation of the same moving object in different frames is established, so that a prediction relation is established;
2. inter-frame predictive coding is carried out on the predictive relation, namely, the motion vector of the motion object after compensation is coded;
3. and compressing the video according to the encoding result to realize compression optimization transmission of cloud video data.
Considering that related data between adjacent frames of video are predicted to obtain inter-frame running compensation, a linear quantization algorithm is adopted to quantize motion estimation, a Huffman coding algorithm is adopted to code quantized motion vectors, each frame of image is not required to be coded in the mode, time and space redundancy can be caused, only the motion trend and trend in the current frame are transmitted to a decoder, the decoder acquires the current frame of image according to the content of the previous frame and the motion trend of the current frame, and the time consumed by the video in the transmission process is effectively reduced. The specific calculation process of linear quantization and huffman coding is a known technology, and this embodiment is not described in detail.
The video data can be compressed and transmitted through the processing, so that the data transmission rate of the intelligent service cloud terminal is improved.
Based on the same inventive concept as the above method, the embodiment of the invention also provides an intelligent service cloud terminal data optimization transmission system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of any one of the above intelligent service cloud terminal data optimization transmission methods when executing the computer program.
In summary, the embodiment of the invention provides an intelligent service cloud terminal data optimization transmission method and system, which are used for acquiring video data, analyzing the data characteristics of each adjacent frame image in the video data, and obtaining the self-adaptive initial search step length of each data sub-block, so that the matching result of different data sub-blocks in each adjacent frame is more accurate, the compression of the video data is completed, and the transmission process of the data is optimized.
The embodiment of the invention relates to an intelligent service cloud terminal data optimization transmission method and system, wherein the data distribution characteristics of each data sub-block of a reference frame are analyzed to construct the data exploration coefficients of each data sub-block, the data exploration coefficients are used for representing the data differences between rows and columns of each data sub-block, the obvious degree of the data characteristics of each data sub-block is reflected on the side surface, the accurate identification of the subsequent matching process is facilitated, and the registration error is reduced;
the method has the advantages that the distribution characteristics of the data in each search window of the corresponding current frame in the reference frame are analyzed, the data sub-blocks of each search step length in the search window obtained through coarse registration are considered to be gradually expanded outwards, namely the complexity of the data information of the moving object in the video data is considered, the exploration influence coefficient of each search window is calculated, whether the search range is increased when registration is carried out is determined through the exploration influence coefficient, the change of the data information of the area far away from the edge of the object is small, the optimal motion estimation area is determined by adding the search area, and the motion compensation of the same data is calculated more accurately; in the mode, each frame of image is not required to be encoded so as to cause redundancy in time and space, the motion trend and trend in the current frame are only required to be transmitted to the decoder, the decoder acquires the current frame of image according to the content of the previous frame and the motion trend of the current frame, the time consumed by video in the transmission process is effectively reduced, the efficiency and quality of video data compression in intelligent service cloud terminal data are improved, and the transmission of the cloud terminal data is optimized.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. The intelligent service cloud terminal data optimization transmission method is characterized by comprising the following steps of:
acquiring video data of an intelligent service cloud terminal;
partitioning each frame of image of video data to obtain each data sub-block; acquiring data exploration coefficients of each data sub-block of the reference frame; determining the search step length and the search window of each data sub-block of the current frame; acquiring data exploration coefficients of each data sub-block and each search window of the current frame;
for each search window of the current frame, the data exploration coefficients of each search step neighborhood data sub-block of the search window are combined into a data exploration coefficient sequence of each search step of the search window; obtaining a characteristic sequence of each search step length of the search window according to the data exploration coefficient sequence of each search step length of the search window and the data exploration coefficient of the data sub-block corresponding to the reference frame; obtaining the data exploration influence coefficient of the search window according to the characteristic sequence of each search step length of the search window and the data exploration coefficient of the data sub-block corresponding to the reference frame; converting the feature sequence of each search step length of the search window into a decimal exploration selection feature value; obtaining the exploration gain step length of the search window according to the data exploration influence coefficient of the search window and the exploration selection characteristic value of each search step length;
calculating motion compensation between adjacent frames of the TTS algorithm according to the exploration gain step length of the search window; compressing and transmitting the video according to the motion compensation of each adjacent frame;
the acquiring the data exploration coefficient of each data sub-block of the reference frame comprises the following steps: for each data sub-block of the reference frame, acquiring row characteristic differences and column characteristic differences of the data sub-blocks; taking the average value of the row characteristic differences and the column characteristic differences as a data exploration coefficient of the data sub-block;
the obtaining the characteristic sequence of each search step length of the search window according to the data exploration coefficient sequence of each search step length of the search window and the data exploration coefficient of the data sub-block corresponding to the reference frame comprises the following steps: for each search window of the current frame, taking the data exploration coefficient of the data sub-block corresponding to the reference frame as a distribution threshold value of the search window; for the data exploration coefficient sequences of each search step length of a search window, marking the distribution coefficient of the data subblocks with the numerical value larger than the distribution threshold value in the data exploration coefficient sequences as 1; marking the distribution coefficient of the data subblocks with the numerical value smaller than the distribution threshold value in the data exploration coefficient sequence as 0; taking the distribution coefficient of each data sub-block of each search step of the search window as the element of the characteristic sequence of each search step of the search window;
the obtaining the data exploration influence coefficient of the search window according to the characteristic sequence of each search step length of the search window and the data exploration coefficient of the corresponding data sub-block of the reference frame comprises the following steps: for each search window of the current frame, obtaining the edge coefficient of the search window according to the search window and the data exploration coefficient of the corresponding data sub-block of the reference frame; obtaining the difference coefficient of the search window according to the characteristic sequence of each search step length of the search window; taking the product of the edge coefficient and the difference coefficient as a data exploration influence coefficient of a search window;
the obtaining the exploration gain step length of the search window according to the data exploration influence coefficient of the search window and the exploration selection characteristic value of each search step length comprises the following steps: when the data exploration influence coefficient of the search window is larger than a judgment threshold value, setting the exploration gain step length of the search window as T, wherein T is the set initial search step length; and when the data exploration influence coefficient of the search window is smaller than the judging threshold value, setting the exploration gain step length of the search window as the search step length of the minimum exploration selection characteristic value.
2. The method for optimizing and transmitting the data of the intelligent service cloud terminal according to claim 1, wherein the step of obtaining the row characteristic difference and the column characteristic difference of the data sub-block comprises the steps of:
for each data sub-block of the reference frame, calculating the DTW distance of each adjacent data sequence of the data sub-block; taking the sum of the DTW distances of all adjacent data sequences of the data sub-block as the line characteristic difference of the data sub-block;
calculating the DTW distance of each adjacent column of data sequence of the data sub-block; and taking the sum of the DTW distances of all adjacent columns of data sequences of the data sub-block as the column characteristic difference of the data sub-block.
3. The method for optimizing data transmission of intelligent service cloud terminal according to claim 1, wherein determining the search step size and the search window of each data sub-block of the current frame comprises:
setting the searching step length of each data sub-block of the current frame;
and taking the data sub-blocks of the current frame as the center, and taking the range which is respectively included by the data sub-blocks with four searching step lengths from top to bottom, left to right as the searching window of the data sub-blocks of the current frame.
4. The method for optimizing and transmitting data of an intelligent service cloud terminal according to claim 1, wherein the obtaining the edge coefficient of the search window according to the search window and the data exploration coefficient of the data sub-block corresponding to the reference frame comprises:
for each search window of the current frame, calculating the absolute value of the difference value of the data exploration coefficients of the corresponding data sub-blocks of the search window and the reference frame, and taking the exponential function value of the absolute value of the difference value as the edge coefficient of the search window.
5. The method for optimizing and transmitting the data of the intelligent service cloud terminal according to claim 1, wherein the obtaining the difference coefficient of the search window according to the characteristic sequence of each search step of the search window comprises the following steps:
for each search window of the current frame, calculating the Hamming distance between each adjacent search step feature sequence of the search window; and taking the Hamming distance average value of all adjacent search steps of the search window as the difference coefficient of the search window.
6. An intelligent service cloud terminal data optimized transmission system comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1-5 when executing the computer program.
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