CN110830806A - Video frame prediction method and device and terminal equipment - Google Patents
Video frame prediction method and device and terminal equipment Download PDFInfo
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- CN110830806A CN110830806A CN201911199761.3A CN201911199761A CN110830806A CN 110830806 A CN110830806 A CN 110830806A CN 201911199761 A CN201911199761 A CN 201911199761A CN 110830806 A CN110830806 A CN 110830806A
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Abstract
The invention is suitable for the technical field of video compression, and provides a video frame prediction method, a video frame prediction device and terminal equipment, wherein the method comprises the following steps: calculating optical flow information between the current frame and the reference frame; inputting the optical flow information, the reference frame and the current frame into a motion compensation network to obtain motion compensation characteristic information; entropy coding and decoding the motion compensation characteristic information, and inputting the motion compensation characteristic information into the motion compensation network to obtain reconstructed optical flow information, a separation convolution kernel and a mask; and obtaining a prediction frame of the current frame based on the reconstructed optical flow information, the separation convolution kernel and the mask. The invention combines the optical flow and the separation convolution to enable the separation convolution kernel to have the effect of self-adaptive position mapping, thereby reducing the size of the separation convolution kernel and improving the performance of video prediction.
Description
Technical Field
The invention belongs to the technical field of video compression, and particularly relates to a video frame prediction method, a video frame prediction device and terminal equipment.
Background
The video compression technology disclosed at present mainly includes prediction by optical flow and separation convolution, however, the optical flow method is suitable for predicting rigid motion such as translation, the separation convolution is suitable for predicting non-rigid motion such as rotation and scaling, and when the video includes both rigid motion and non-rigid motion, neither method can be used for prediction, and the video prediction performance is low.
Therefore, a new technical solution is needed to solve the above problems.
Disclosure of Invention
In view of this, embodiments of the present invention provide a video frame prediction method and a terminal device, so as to solve the problem of low video prediction performance in the prior art.
A first aspect of an embodiment of the present invention provides a video frame prediction method, including:
calculating optical flow information between the current frame and the reference frame;
inputting the optical flow information, the reference frame and the current frame into a motion compensation network to obtain motion compensation characteristic information;
entropy coding and decoding the motion compensation characteristic information, and inputting the motion compensation characteristic information into the motion compensation network to obtain reconstructed optical flow information, a separation convolution kernel and a mask;
and obtaining a prediction frame of the current frame based on the reconstructed optical flow information, the separation convolution kernel and the mask.
A second aspect of the embodiments of the present invention provides a video frame prediction apparatus, including:
the optical flow module is used for calculating optical flow information between the current frame and the reference frame;
the motion compensation module is used for inputting the optical flow information, the reference frame and the current frame into a motion compensation network to obtain motion compensation characteristic information;
the entropy coding and decoding module is used for performing entropy coding and entropy decoding on the motion compensation characteristic information and then inputting the motion compensation characteristic information into the motion compensation network to obtain reconstructed optical flow information, a separation convolution kernel and a mask;
and the prediction frame module is used for obtaining a prediction frame of the current frame based on the reconstructed optical flow information, the separation convolution kernel and the mask.
A third aspect of embodiments of the present invention provides a video frame prediction terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as provided in the first aspect above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the invention combines the optical flow and the separation convolution to enable the separation convolution kernel to have the effect of self-adaptive position mapping, thereby reducing the size of the separation convolution kernel and improving the performance of video prediction.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating an implementation of a video frame prediction method according to an embodiment of the present invention;
FIG. 2 is a diagram of an apparatus for predicting video frames according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a video frame prediction terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Example one
Fig. 1 shows a flow of implementing a video frame prediction method according to an embodiment of the present invention, where an execution subject of the method may be a terminal device, and details are as follows:
in step S101, optical flow information between the current frame and the reference frame is calculated.
Optionally, a spatial position mapping relationship between pixels of the current frame image and pixels of the reference frame image is calculated to obtain optical flow information.
Specifically, the optical flow is to use the change of pixels in the image sequence in the time domain and the correlation between adjacent frames to find the correlation between two adjacent frames, so as to calculate the motion information of the object between the adjacent frames: and inputting the current frame and the reference frame into a preset optical flow network to obtain optical flow information. Further, the optical flow network includes two network structures: FlowNeTS (FlowNetSimple) and FlowNetC (FlowNetCorr). The optical flow network FlowNet S directly overlaps and inputs two images according to channel dimensions, and the network structure of the FlowNet S only has convolution layers; the optical flow network FlowNet C firstly extracts the characteristics of the two input images respectively and then calculates the correlation of the characteristics, namely the characteristics of the two images are subjected to convolution operation in a space dimension.
And step S102, inputting the optical flow information, the reference frame and the current frame into a motion compensation network to obtain motion compensation characteristic information.
Optionally, the motion compensation network comprises an up-sampling layer, a down-sampling layer, an encoding network and a decoding network. And further, inputting the optical flow information, the reference frame and the current frame into a motion compensation network, and performing down-sampling operation and convolution operation on a down-sampling layer to obtain motion compensation characteristic information.
Step S103, entropy coding and decoding the motion compensation characteristic information, and inputting the motion compensation characteristic information into the motion compensation network to obtain reconstructed optical flow information, a separation convolution kernel and a mask.
Optionally, entropy coding is performed on the motion compensation feature information to obtain a compressed bit stream, and the compressed bit stream is stored. Further, the stored compressed bit stream is entropy decoded and then input to the motion compensation network to obtain reconstructed optical flow information, a separate convolution kernel and a mask. The above coding may be an entropy coding scheme such as Shannon (Shannon) coding, Huffman (Huffman) coding, or arithmetic coding (arithmeticcoding), and is not limited herein.
And step S104, obtaining a prediction frame of the current frame based on the reconstructed optical flow information, the separation convolution kernel and the mask.
Optionally, a warp prediction frame is obtained after performing a warp operation on the reference frame according to the reconstructed optical flow information. Specifically, according to the reconstructed optical flow information, a warp prediction frame is obtained by converting a reference frame warp (affine transformation of an image) to a specified position.
Further, the reference frame and the separation convolution kernel are subjected to separation convolution operation to obtain a separation convolution prediction frame. Specifically, each pixel in the reference frame is convolved with the separate convolution kernel to obtain a separate convolution prediction frame.
And further, fusing the warp predicted frame and the separated convolution predicted frame according to the mask to obtain a predicted frame of the current frame. Optionally, the mask, warp predicted frame, separate convolution predicted frame, and predicted frame of the current frame satisfy the following relation:
the Warp prediction frame × mask + split convolution prediction frame × (1-mask) — the prediction frame of the current frame.
Wherein, the mask is a two-dimensional matrix, and the value in the mask is 0 or 1.
Optionally, after obtaining the predicted frame of the current frame based on the reconstructed optical flow information and the separation convolution kernel, the method further includes:
subtracting the current frame from the predicted frame of the current frame to obtain a residual error;
inputting the residual error into a residual error compression network to obtain a decompressed residual error; optionally, wherein the residual compression network is a neural network comprising an upsampling layer, an encoding network, a decoding network, and a downsampling layer. Inputting the residual error into a residual error compression network, coding the residual error to obtain a residual error bit stream, decoding the residual error bit stream based on a decoding network, and down-sampling to obtain a decompressed residual error.
And adding the predicted frame of the current frame and the decompressed residual error to obtain a reconstructed frame of the current frame.
In this embodiment, the separate convolution kernel has the effect of adaptive position mapping by combining the optical flow and the separate convolution, so that the size of the separate convolution kernel can be reduced, and the performance of video prediction is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Example two
Fig. 2 is a block diagram illustrating a structure of a video frame prediction apparatus according to an embodiment of the present invention, and only a part related to the embodiment of the present invention is shown for convenience of description. The video frame prediction apparatus 2 includes: an optical flow module 21, a motion compensation module 22, an entropy coding and decoding module 23, and a predicted frame module 24.
The optical flow module 21 is configured to calculate optical flow information between the current frame and the reference frame;
a motion compensation module 22, configured to input the optical flow information, the reference frame, and the current frame into a motion compensation network to obtain motion compensation feature information;
an entropy coding and decoding module 23, configured to perform entropy coding and entropy decoding on the motion compensation feature information, and then input the motion compensation feature information into the motion compensation network to obtain reconstructed optical flow information, a separation convolution kernel, and a mask;
and a predicted frame module 24, configured to obtain a predicted frame of the current frame based on the reconstructed optical flow information, the separation convolution kernel, and the mask.
Optionally, the optical flow module 21 comprises:
and the optical flow information calculation unit is used for calculating the spatial position mapping relation between the pixels of the current frame image and the pixels of the reference frame image to obtain optical flow information.
Optionally, the predicted frame module 24 comprises:
a warp unit, configured to perform a warp operation on the reference frame according to the reconstructed optical flow information to obtain a warp prediction frame;
a separation convolution unit, configured to perform a separation convolution operation on the reference frame and the separation convolution kernel to obtain a separation convolution prediction frame;
and the fusion unit is used for fusing the warp prediction frame and the separated convolution prediction frame according to the mask to obtain a prediction frame of the current frame.
Optionally, the mask, warp predicted frame, separate convolution predicted frame, and predicted frame of the current frame satisfy the following relation:
the Warp prediction frame × mask + split convolution prediction frame × (1-mask) — the prediction frame of the current frame.
Optionally, the motion compensation module 22 comprises:
and the down-sampling and convolution unit is used for inputting the optical flow information, the reference frame and the current frame into a motion compensation network to perform down-sampling operation and convolution operation so as to obtain motion compensation characteristic information.
Optionally, the video frame prediction apparatus 2 further includes:
and the reconstructed frame module is used for subtracting the predicted frame of the current frame from the current frame to obtain a residual error, inputting the residual error into a residual error compression network to obtain a decompressed residual error, and adding the predicted frame of the current frame and the decompressed residual error to obtain a reconstructed frame of the current frame.
EXAMPLE III
Fig. 3 is a schematic diagram of a video frame prediction terminal device according to an embodiment of the present invention. As shown in fig. 3, the video frame prediction terminal device 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32, such as a video frame prediction program, stored in the memory 31 and executable on the processor 30. The processor 30, when executing the computer program 32, implements the steps of the various embodiments of the video frame prediction method described above, such as the steps 101 to 104 shown in fig. 1. Alternatively, the processor 30 implements the functions of the modules/units in the device embodiments, such as the functions of the modules 21 to 24 shown in fig. 2, when executing the computer program 32.
Illustratively, the computer program 32 may be divided into one or more modules, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 32 in the video frame prediction terminal device 3. For example, the computer program 32 may be divided into an optical flow module, a motion compensation module, an entropy coding and decoding module, and a predicted frame module, and each module has the following specific functions:
the optical flow module is used for calculating optical flow information between the current frame and the reference frame;
the motion compensation module is used for inputting the optical flow information, the reference frame and the current frame into a motion compensation network to obtain motion compensation characteristic information;
the entropy coding and decoding module is used for performing entropy coding and entropy decoding on the motion compensation characteristic information and then inputting the motion compensation characteristic information into the motion compensation network to obtain reconstructed optical flow information, a separation convolution kernel and a mask;
and the prediction frame module is used for obtaining a prediction frame of the current frame based on the reconstructed optical flow information, the separation convolution kernel and the mask.
The video frame prediction terminal device 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The video frame prediction terminal device may include, but is not limited to, a processor 30 and a memory 31. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the video frame prediction terminal device 3, and does not constitute a limitation of the video frame prediction terminal device 3, and may include more or less components than those shown, or combine some components, or different components, for example, the video frame prediction terminal device may further include an input-output device, a network access device, a bus, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the video frame prediction terminal device 3, such as a hard disk or a memory of the video frame prediction terminal device 3. The memory 31 may be an external storage device of the video frame prediction terminal device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), or the like provided on the video frame prediction terminal device 3. Further, the memory 31 may include both an internal storage unit and an external storage device of the video frame prediction terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the video frame prediction terminal device. The above-mentioned memory 31 may also be used to temporarily store data that has been output or is to be output.
As can be seen from the above, in the embodiment, the separate convolution kernel has the effect of adaptive location mapping by combining the optical flow and the separate convolution, so that the size of the separate convolution kernel can be reduced, and the performance of video prediction is improved.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the above modules or units is only one logical function division, and there may be other division manners in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for video frame prediction, comprising:
calculating optical flow information between the current frame and the reference frame;
inputting the optical flow information, the reference frame and the current frame into a motion compensation network to obtain motion compensation characteristic information;
entropy coding and decoding the motion compensation characteristic information, and inputting the motion compensation characteristic information into the motion compensation network to obtain reconstructed optical flow information, a separation convolution kernel and a mask;
and obtaining a prediction frame of the current frame based on the reconstructed optical flow information, the separation convolution kernel and the mask.
2. The video frame prediction method of claim 1, wherein said calculating optical flow information between the current frame and the reference frame comprises:
and calculating the spatial position mapping relation between the pixels of the current frame image and the pixels of the reference frame image to obtain optical flow information.
3. The video frame prediction method of claim 1, wherein said deriving the predicted frame of the current frame based on the reconstructed optical flow information, the separate convolution kernel, and the mask comprises:
performing warp operation on the reference frame according to the reconstructed optical flow information to obtain a warp prediction frame;
performing separation convolution operation on the reference frame and the separation convolution kernel to obtain a separation convolution prediction frame;
and fusing the warp predicted frame and the separated convolution predicted frame according to the mask to obtain a predicted frame of the current frame.
4. The method of claim 3, wherein said fusing the warp predicted frame and the separate convolution predicted frame to obtain the predicted frame of the current frame according to the mask comprises:
the mask, warp predicted frame, separate convolution predicted frame and predicted frame of the current frame satisfy the relation:
the Warp prediction frame × mask + split convolution prediction frame × (1-mask) — the prediction frame of the current frame.
5. The video frame prediction method of claim 1, further comprising, after said deriving a predicted frame for a current frame based on said reconstructed optical flow information and a separate convolution kernel:
subtracting the predicted frame of the current frame from the current frame to obtain a residual error;
inputting the residual error into the residual error compression network to obtain a decompressed residual error;
and adding the predicted frame of the current frame and the decompressed residual error to obtain a reconstructed frame of the current frame.
6. A video frame prediction apparatus, comprising:
the optical flow module is used for calculating optical flow information between the current frame and the reference frame;
the motion compensation module is used for inputting the optical flow information, the reference frame and the current frame into a motion compensation network to obtain motion compensation characteristic information;
the entropy coding and decoding module is used for performing entropy coding and entropy decoding on the motion compensation characteristic information and then inputting the motion compensation characteristic information into the motion compensation network to obtain reconstructed optical flow information, a separation convolution kernel and a mask;
and the prediction frame module is used for obtaining a prediction frame of the current frame based on the reconstructed optical flow information, the separation convolution kernel and the mask.
7. The video frame prediction apparatus of claim 6, wherein the optical flow module comprises:
and the optical flow information calculation unit is used for calculating the spatial position mapping relation between the pixels of the current frame image and the pixels of the reference frame image to obtain optical flow information.
8. The video frame prediction device of claim 6, wherein the predicted frame module comprises:
a warp unit, configured to perform a warp operation on the reference frame according to the reconstructed optical flow information to obtain a warp prediction frame;
the separation convolution unit is used for carrying out separation convolution operation on the reference frame and the separation convolution kernel to obtain a separation convolution prediction frame;
and the Mask unit is used for fusing the warp predicted frame and the separated convolution predicted frame according to the Mask to obtain a predicted frame of the current frame.
9. Video frame prediction terminal device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor realizes the steps of the method according to any of the claims 1 to 5 when executing said computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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