CN113286144B - Method, device and medium for rapid decision making of CU (Unit of computer) division based on Gabor - Google Patents
Method, device and medium for rapid decision making of CU (Unit of computer) division based on Gabor Download PDFInfo
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- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
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Abstract
The invention discloses a fast decision-making method, a device and a medium for partitioning a CU (Central Unit) based on a Gabor, wherein the method comprises the following steps: acquiring the block area of a current coding unit; when the block area meets a preset first condition, performing vertical filtering processing and horizontal filtering processing on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit; calculating a first energy of the vertically filtered image and a second energy of the horizontally filtered image; determining a target division mode according to the first energy and the second energy; and dividing the current coding unit according to the target division mode. According to the embodiment of the invention, filtering in the horizontal and vertical directions is respectively carried out on the current coding unit, the horizontal and vertical texture features are respectively extracted, and the horizontal or vertical division mode is skipped according to the texture difference, so that the division complexity can be reduced, the division efficiency can be improved, and the method can be widely applied to the technical field of data processing.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a rapid decision-making method, a rapid decision-making device and a rapid decision-making medium for partitioning a CU (Central Unit) based on a Gabor (Gabor).
Background
The chinese audio video coding standard (AVS) work was established in 2002 and is intended to provide high quality standards for compression, decompression, processing and presentation of digital audio and video. With the advent of the 5G communication era, new application scenarios such as 4K/8K ultra-high-definition video, Virtual Reality (VR) video, cloud games and the like have brought demands on video coding technologies. The AVS working group started the work of the third generation audio Video coding standard (AVS3), and finished the preliminary formulation in 2019 for the international vvc (versatile Video coding) standard.
Compared with the video coding standards of the previous generation, AVS2 and HEVC, AVS3 adds a lot of new technologies. For example, an intra-frame prediction filter (IPF) is added in intra-frame coding, so that the spatial correlation among pixels is effectively enhanced, and the intra-frame prediction precision is improved; and the original 30 angle prediction modes are expanded to 62, so that the angle prediction is more accurate. Whereas in inter-frame coding, affine motion compensation uses 4-parameter and 6-parameter motion models, expressing richer motion models such as scaling and rotation. The most varied is the partitioning in the base block structure. Both the AVS2 and HEVC adopt a Quadtree (QT) partition, which divides a CU into four sub-CUs. Whereas the AVS3 standard, in addition to supporting quadtree partitioning, adds Binary Tree (BT) and EQT partitioning. The BT divides a CU into a left sub CU, a right sub CU, an upper sub CU and a lower sub CU; the EQT includes two i-shaped horizontal and vertical partitioning modes, and partitions one CU into four sub-CUs.
In AVS3, the block partitioning decision takes a recursive partitioning decision starting from the LCU from top to bottom. For the current CU, the encoding end needs to traverse all the partition modes allowed by the encoding end, and determines the optimal partition mode according to the rate-distortion cost of each partition mode.
Disclosure of Invention
In view of this, embodiments of the present invention provide a Gabor-based CU partition fast decision method, apparatus, and medium to reduce partition complexity and improve partition efficiency.
One aspect of the present invention provides a Gabor-based CU partitioning fast decision method, including:
acquiring the block area of a current coding unit;
when the block area meets a preset first condition, performing vertical filtering processing and horizontal filtering processing on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
calculating a first energy of the vertically filtered image and a second energy of the horizontally filtered image;
determining a target division mode according to the first energy and the second energy;
and dividing the current coding unit according to the target division mode.
Optionally, the method further comprises: a step of judging whether the block area of the current coding unit meets a preset first condition, specifically:
judging whether the block area of the current coding unit is larger than or equal to 1024, if so, executing vertical filtering processing and horizontal filtering processing on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
otherwise, traversing all available division modes of the current coding unit, and dividing the current coding unit according to the available division modes obtained by traversal.
Optionally, the performing a vertical filtering process and a horizontal filtering process on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit includes:
and generating a convolution core through a Gabor function to perform convolution operation on the image pixel of the current coding unit, and respectively extracting a vertical filtering image and a horizontal filtering image of the current coding unit.
Optionally, in the step of calculating a first energy of the vertical filtering image and a second energy of the horizontal filtering image, the calculation formula of the first energy and the second energy is:
Wherein, E img The height is the filtered image height, the width is the filtered image width, and the img is the pixel value of the filtered image.
Optionally, the method further comprises:
and determining the texture density of the current coding unit in the vertical direction or the horizontal direction according to the first energy or the second energy.
Optionally, the method further comprises:
calculating a ratio between the second energy and the first energy;
when the ratio is larger than a preset first threshold value, determining that the texture of the current coding unit is a horizontal texture;
when the ratio is smaller than a preset first threshold value, determining that the texture of the current coding unit is a vertical texture;
and when the ratio is equal to a preset first threshold value, determining that the texture of the current coding unit comprises a horizontal texture and a vertical texture.
Optionally, the method further comprises:
when the texture of the current coding unit is a horizontal texture, forbidding vertical binary tree division and horizontal EQT division;
and when the texture of the current coding unit is a vertical texture, disabling horizontal binary tree partitioning and vertical partitioning.
Another aspect of the embodiments of the present invention provides a Gabor-based CU partition fast decision device,
The method comprises the following steps:
an obtaining module, configured to obtain a block area of a current coding unit;
the filtering processing module is used for executing vertical filtering processing and horizontal filtering processing on the current coding unit when the block area meets a preset first condition to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
an energy calculation module for calculating a first energy of the vertical filtered image and a second energy of the horizontal filtered image;
the determining module is used for determining a target division mode according to the first energy and the second energy;
and the dividing module is used for dividing the current coding unit according to the target dividing mode.
Another aspect of the embodiments of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
A computer-readable storage medium, storing a program which is executed by a processor to implement the method as set forth above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
The embodiment of the invention firstly obtains the block area of the current coding unit; when the block area meets a preset first condition, performing vertical filtering processing and horizontal filtering processing on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit; calculating a first energy of the vertically filtered image and a second energy of the horizontally filtered image; determining a target division mode according to the first energy and the second energy; and dividing the current coding unit according to the target division mode. According to the embodiment of the invention, filtering in the horizontal and vertical directions is respectively carried out on the current coding unit, the horizontal and vertical texture features of the current coding unit are respectively extracted, and the horizontal or vertical division mode is skipped according to the difference of the textures, so that the division complexity can be reduced and the division efficiency can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of method steps provided by an embodiment of the present invention;
fig. 2 is a diagram illustrating the filtering results in different directions according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Aiming at the problems in the prior art, the embodiment of the invention provides a rapid decision algorithm based on a Gabor filter. Filtering the current CU in the horizontal direction and the vertical direction by using a Gabor filter, extracting horizontal texture features and vertical texture features of the current CU respectively, and skipping the dividing mode in the horizontal direction or the vertical direction according to the difference of textures
Specifically, an embodiment of the present invention provides a Gabor-based CU partition fast decision method, including:
acquiring the block area of a current coding unit;
when the block area meets a preset first condition, performing vertical filtering processing and horizontal filtering processing on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
Calculating a first energy of the vertically filtered image and a second energy of the horizontally filtered image;
determining a target division mode according to the first energy and the second energy;
and dividing the current coding unit according to the target division mode.
Optionally, the method further comprises: a step of judging whether the block area of the current coding unit meets a preset first condition, specifically:
judging whether the block area of the current coding unit is larger than or equal to 1024, if so, executing vertical filtering processing and horizontal filtering processing on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
otherwise, traversing all available division modes of the current coding unit, and dividing the current coding unit according to the available division modes obtained by traversal.
Optionally, the performing a vertical filtering process and a horizontal filtering process on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit includes:
and generating a convolution core through a Gabor function to perform convolution operation on the image pixel of the current coding unit, and respectively extracting a vertical filtering image and a horizontal filtering image of the current coding unit.
Optionally, in the step of calculating a first energy of the vertical filtering image and a second energy of the horizontal filtering image, the calculation formula of the first energy and the second energy is:
wherein E is img The height is the height of the filtered image, the width is the width of the filtered image, and the img is the pixel value of the filtered image;
optionally, the method further comprises:
and determining the texture density of the current coding unit in the vertical direction or the horizontal direction according to the first energy or the second energy.
Optionally, the method further comprises:
calculating a ratio between the second energy and the first energy;
when the ratio is larger than a preset first threshold value, determining that the texture of the current coding unit is a horizontal texture;
when the ratio is smaller than a preset first threshold value, determining that the texture of the current coding unit is a vertical texture;
and when the ratio is equal to a preset first threshold value, determining that the texture of the current coding unit comprises a horizontal texture and a vertical texture.
Optionally, the method further comprises:
when the texture of the current coding unit is a horizontal texture, forbidding vertical binary tree division and horizontal EQT division;
And when the texture of the current coding unit is a vertical texture, disabling horizontal binary tree partitioning and vertical partitioning.
Another aspect of the embodiments of the present invention provides a Gabor-based CU partition fast decision device,
the method comprises the following steps:
an obtaining module, configured to obtain a block area of a current coding unit;
the filtering processing module is used for executing vertical filtering processing and horizontal filtering processing on the current coding unit when the block area meets a preset first condition to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
an energy calculation module for calculating a first energy of the vertical filtered image and a second energy of the horizontal filtered image;
the determining module is used for determining a target division mode according to the first energy and the second energy;
and the dividing module is used for dividing the current coding unit according to the target dividing mode.
Another aspect of the embodiments of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
A computer-readable storage medium, storing a program for execution by a processor to implement a method as described above.
The following describes in detail a specific implementation process of the embodiment of the present invention with reference to the drawings in the specification:
as shown in fig. 1, fig. 1 is a flowchart for illustrating an implementation process of a Gabor-based CU partition fast decision method provided by an embodiment of the present invention, and the implementation process specifically includes the following steps:
(1) judging whether the area of the current CU is greater than or equal to 32 x 32, if not, not performing quick CU division judgment, and turning to the step (6); otherwise, turning to the step (2);
(2) and respectively carrying out horizontal Gabor filtering and vertical Gabor filtering on the current CU to respectively obtain filtered images hor _ img and ver _ img.
(3) Respectively calculating the energy E _ ver and the energy E _ hor of the filtered image;
(4) if the energy E _ ver > E _ hor × th of the filtering result graph, forbidding horizontal BT division and horizontal EQT division, and turning to the step (6); otherwise, turning to the step (5);
(5) if the energy E _ hor > E _ ver × th of the filtering result graph, forbidding vertical BT division and vertical EQT division;
(6) and traversing the available partition modes of the current CU, and recursively calculating the rate-distortion cost of each partition mode to obtain the optimal partition mode.
It should be noted that Gabor is a complex sine function modulated by a gaussian function, which is a window function of short-time fourier transform, and the related function is Gabor transform. The expression for the Fulvin chord function may be expressed as:
Wherein x is 0 ＝x cos θ+y sin θ，y 0 ＝-xin θ+y cos θ
Wherein x and y are spatial coordinates of the pixel; λ is the wavelength of the sine function; θ represents the filtering direction of the Gabor filter; gamma is a space aspect ratio and determines the ellipticity of the shape of the Gabor function; ψ is the phase shift of the sine function.
According to the embodiment of the invention, the convolution kernel is generated through the Gabor function to carry out convolution operation on the pixels of the image, so that the texture characteristics of the image in the filtering direction theta can be extracted. Fig. 2 shows the result of vertical and horizontal Gabor filtering of two images with vertical and horizontal texture respectively. After the processing of the filter of the embodiment of the invention, the texture of the image in the filtering direction (vertical texture or horizontal texture) is highlighted and appears white; while the texture in the non-filtering direction (vertical texture or horizontal texture) is suppressed and appears black.
It should be noted that the partition of a CU (coding unit) has a great relationship with the texture characteristics thereof, and if the texture of the current CU is a horizontal texture, a vertical partition manner is not basically selected, and similarly, a CU of a vertical texture is not selected a horizontal partition manner. The Gabor filter can extract texture features of the corresponding direction of the image. By utilizing the characteristics, the invention can design a CU partition quick decision algorithm based on the Gabor filter.
In the embodiment of the present invention, Gabor filtering is performed on a current CU in a horizontal direction and a vertical direction, respectively, and energy of a filtered image is calculated according to the following formula:
wherein E is img The height is the filtered image height, the width is the filtered image width, and the img is the pixel value of the filtered image.
According to the embodiment of the invention, the texture density of the current CU in the direction can be known according to the energy E of the filter map corresponding to the direction. When the ratio of the horizontal filter map energy to the vertical filter map energy is greater than a certain threshold th, it indicates that the texture of the current CU is mainly the horizontal texture, and the vertical partitioning mode, including the vertical binary tree partitioning and the vertical EQT partitioning, may be skipped. Similarly, when the ratio of the energy of the vertical filter map to the energy of the horizontal filter map is smaller than a threshold th, the horizontal binary tree partition and the horizontal EQT partition are skipped. If neither of the above two conditions is satisfied, it indicates that the difference between the horizontal texture density and the vertical texture density of the current CU is not obvious, and rate-distortion costs need to be calculated for all available partition modes without skipping any partition mode.
In addition, some of the division restriction conditions in AVS3 are set as follows:
■min_qt_size(8)(max_qt_size is always the size of LCU,128)
■min_bt_size(8)/max_bt_size(128)
■min_eqt_size(8/16)/max_eqt_size(64)
■max_split_depth(6)
since the texture features of the small block CU are relatively insignificant and the partition depth is large, there are fewer available partition modes and the amount of computation to skip some partition modes is not large. The fast algorithm is therefore only applied to blocks with an area greater than or equal to 32 x 32.
In addition, the first threshold th is selected according to the size of the QP, and when the QP is small, the texture details of the CU are more obvious, so that a smaller Y threshold can be selected. And when the QP is large, the texture detail of the CU is insufficient, and a large threshold needs to be selected. It is necessary to select a better threshold value according to experimental determination.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A fast decision-making method for CU partition based on Gabor is characterized by comprising the following steps:
acquiring the block area of a current coding unit;
when the block area meets a preset first condition, performing vertical filtering processing and horizontal filtering processing on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
calculating a first energy of the vertically filtered image and a second energy of the horizontally filtered image;
determining a target division mode according to the first energy and the second energy;
dividing the current coding unit according to the target division mode;
after the step of obtaining the block area of the current coding unit, the method further includes a step of judging whether the block area of the current coding unit meets a preset first condition, where the step specifically includes:
judging whether the block area of the current coding unit is larger than or equal to 1024, if so, executing vertical filtering processing and horizontal filtering processing on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
otherwise, traversing all available partition modes of the current coding unit, and partitioning the current coding unit according to the available partition modes obtained by traversal;
In the step of calculating a first energy of the vertical filtering image and a second energy of the horizontal filtering image, a calculation formula of the first energy and the second energy is as follows:
wherein, E img The height is the height of the filtered image, the width is the width of the filtered image, and the img is the pixel value of the filtered image;
the step of determining a target partitioning pattern based on the first energy and the second energy further comprises:
calculating a ratio between the second energy and the first energy;
when the ratio is larger than a preset first threshold value, determining that the texture of the current coding unit is a horizontal texture;
when the ratio is smaller than a preset first threshold value, determining that the texture of the current coding unit is a vertical texture;
when the ratio is equal to a preset first threshold value, determining that the texture of the current coding unit comprises a horizontal texture and a vertical texture;
when the texture of the current coding unit is a horizontal texture, disabling vertical binary tree division and vertical EQT division;
when the texture of the current coding unit is a vertical texture, forbidding horizontal binary tree division and horizontal EQT division;
When the texture of the current coding unit includes a horizontal texture and a vertical texture, rate-distortion costs are calculated for all available partition modes without disabling any partition mode.
2. The Gabor-based CU partition fast decision method according to claim 1, wherein the performing a vertical filtering process and a horizontal filtering process on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit comprises:
and generating a convolution core through a Gabor function to perform convolution operation on the image pixel of the current coding unit, and respectively extracting a vertical filtering image and a horizontal filtering image of the current coding unit.
3. A Gabor-based CU partition fast decision apparatus, comprising:
an obtaining module, configured to obtain a block area of a current coding unit;
the filtering processing module is used for executing vertical filtering processing and horizontal filtering processing on the current coding unit when the block area meets a preset first condition to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
an energy calculation module for calculating a first energy of the vertical filtered image and a second energy of the horizontal filtered image;
The determining module is used for determining a target division mode according to the first energy and the second energy;
the dividing module is used for dividing the current coding unit according to the target dividing mode;
the filtering processing module is specifically configured to:
judging whether the block area of the current coding unit is larger than or equal to 1024, if so, executing vertical filtering processing and horizontal filtering processing on the current coding unit to obtain a vertical filtering image and a horizontal filtering image of the current coding unit;
otherwise, traversing all available partition modes of the current coding unit, and partitioning the current coding unit according to the available partition modes obtained by traversal;
the energy calculation module is specifically configured to:
calculating a first energy of the vertical filtering image and a second energy of the horizontal filtering image, wherein the calculation formula of the first energy and the second energy is as follows:
wherein E is img For the energy value of the filtered image, height is the filtered imageThe image height, width is the image width after filtering, and img is the pixel value of the image after filtering;
the determination module is specifically configured to:
calculating a ratio between the second energy and the first energy;
When the ratio is larger than a preset first threshold value, determining that the texture of the current coding unit is a horizontal texture;
when the ratio is smaller than a preset first threshold value, determining that the texture of the current coding unit is a vertical texture;
when the ratio is equal to a preset first threshold value, determining that the texture of the current coding unit comprises a horizontal texture and a vertical texture;
when the texture of the current coding unit is a horizontal texture, disabling vertical binary tree division and vertical EQT division;
when the texture of the current coding unit is a vertical texture, forbidding horizontal binary tree division and horizontal EQT division;
when the texture of the current coding unit includes a horizontal texture and a vertical texture, rate-distortion costs are calculated for all available partition modes without disabling any partition mode.
4. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program realizes the method of any one of claims 1-2.
5. A computer-readable storage medium, characterized in that the storage medium stores a program, which is executed by a processor to implement the method according to any one of claims 1-2.
Priority Applications (1)
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