CN113473135B - Intra-frame prediction method, device and medium for nonlinear texture - Google Patents
Intra-frame prediction method, device and medium for nonlinear texture Download PDFInfo
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- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
- H04N19/157—Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
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- H04N19/182—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a pixel
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Abstract
The present disclosure relates to a nonlinear texture-oriented intra prediction method, device and medium, the method is used in an intra prediction module in the field of image and video coding, and the method includes: determining a current intra prediction mode; predicting a non-linear texture in the intra prediction module using predictive modeling comprising a quadratic function; the position of the reference pixel is derived from the result of predicting the nonlinear texture and the predicted pixel value is generated from the interpolation of the reference pixel. The method solves the problem of high-efficiency prediction modeling of the image and video coding and decoding standard intra-frame prediction module facing nonlinear texture content. The method and the device can generate the high-fidelity prediction signal close to the original signal, reduce the prediction residual error and improve the coding efficiency.
Description
Technical Field
The present disclosure relates to the field of intra prediction technology, and more particularly, to a nonlinear texture-oriented intra prediction method, apparatus, and medium.
Background
In image and video coding, spatial redundancy of signals is reduced mainly through intra-frame prediction. Mainstream video codec standards (e.g., VVC, AVS3, etc.) define a series of intra-prediction angle modes in an intra-prediction module to generate predicted content. However, the current angle prediction mode can only generate linear textures, and cannot accurately and efficiently model nonlinear textures. Therefore, the application provides a nonlinear texture-oriented intra-frame prediction algorithm, which improves the efficiency of intra-frame prediction and improves the coding efficiency.
The intra prediction modes of VVC are 67 modes in total, including DC mode, planar mode and other 65 angular prediction modes. The intra-prediction angle mode defines a direction of intra-prediction, i.e., a direction through which reference pixels are projected onto corresponding positions of a prediction block to form the prediction block. Taking fig. 1 as an example, the angle α corresponds to a tangential angle of a certain intra-prediction direction, and the pixel intensities at positions where the angle α passes through are the same. Thus, for the predicted pixel p [ x0] [ y0], its corresponding reference pixel position c can be derived from the following equation, and its pixel intensity can be interpolated by a multi-tap filter.
c＝x 0 -tanα*y 0 (1)
p[x 0 ][y 0 ]＝f[0]*p[c][0]+f[1]*p[c][1]+f[2]*p[c][2]+f[3]*p[c][3] (2)。
Disclosure of Invention
The method aims to solve the technical problem that the image and video coding standard in the prior art cannot generate nonlinear texture content close to an original signal in an intra-frame prediction module.
To achieve the above technical object, the present disclosure provides a method for intra prediction for nonlinear texture, including:
determining a current intra prediction mode;
predicting a non-linear texture in the intra prediction module using predictive modeling comprising a quadratic function;
the position of the reference pixel is derived from the result of predicting the nonlinear texture and the predicted pixel value is generated from the interpolation of the reference pixel.
Further, the intra prediction modes specifically include:
a normal prediction mode close to the reference pixel and an extended prediction mode far from the reference pixel.
Further, the prediction is modeled as a model using a quadratic function or a model using a linear combination of a first and a second function.
Further, predicting a nonlinear texture in the intra prediction module using predictive modeling including a quadratic function specifically includes:
the predictive modeling including quadratic functions is represented using two angular prediction modes to predict nonlinear textures.
Further, the two angular prediction modes belong to either a set of vertical prediction modes or a set of horizontal prediction modes.
Further, the deriving the reference pixel according to the result of predicting the nonlinear texture specifically includes:
when the two angular prediction modes belong to the same set of vertical prediction modes,
using the formula
Determining a reference pixel position c;
wherein, the angle alpha corresponds to the tangential direction when the quadratic function enters the prediction block, and the angle beta corresponds to the tangential direction when the quadratic function leaves the prediction block;
x 0 representing the abscissa, y 0 Indicating the ordinate, h indicating the height of the intra prediction block;
using the formula
p[x 0 ][y 0 ]＝f[0]*p[c][0]+f[1]*p[c][1]+f[2]*p[c][2]+f[3]*p[c][3]
Determining a location of an intra-predicted pixel;
wherein p [ x0] [ y0] represents an intra-prediction pixel;
when the two angular prediction modes belong together to a set of horizontal prediction modes,
using the formula
Determining a reference pixel position c;
wherein, the angle alpha corresponds to the tangential direction when the quadratic function enters the prediction block, and the angle beta corresponds to the tangential direction when the quadratic function leaves the prediction block;
x 0 representing the abscissa, y 0 Representing the ordinate, w representing the width of the intra prediction block;
using the formula
p[x 0 ][y 0 ]＝f[0]*p[0][c]+f[1]*p[0][c+1]+f[2]*p[0][c+2]+f[3]*p[0][c+3]
Determining a location of an intra-predicted pixel;
where p [ x0] [ y0] represents an intra-predicted pixel.
Further, the generating a predicted pixel value according to the position of the reference pixel specifically adopts a gaussian interpolation or a cubic spline interpolation method to generate the predicted pixel value according to the position of the reference pixel.
Further, the combination method of the angle prediction modes is based on a model of a statistical rule and is also based on a data driving method of deep learning or machine learning.
To achieve the above technical object, the present disclosure also provides a computer storage medium having stored thereon a computer program for implementing the steps of the above nonlinear texture oriented intra prediction method when the computer program is executed by a processor.
To achieve the above technical objective, the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the steps of the computer program to implement the above non-linear texture oriented intra prediction method in real time.
The beneficial effects of the present disclosure are:
the method solves the problem of high-efficiency prediction modeling of the image and video coding and decoding standard intra-frame prediction module facing nonlinear texture content. The method and the device can generate the high-fidelity prediction signal close to the original signal, reduce the prediction residual error and improve the coding efficiency.
Drawings
Fig. 1 shows a schematic diagram of a prior art intra prediction method;
FIG. 2 shows a schematic flow diagram of embodiment 1 of the present disclosure;
fig. 3 shows a schematic diagram of an intra prediction method of embodiment 1 of the present disclosure;
fig. 4 shows a schematic structural diagram of embodiment 3 of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
Various structural schematic diagrams according to embodiments of the present disclosure are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated for clarity of presentation and may have been omitted. The shapes of the various regions, layers and relative sizes, positional relationships between them shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.
Embodiment one:
as shown in fig. 2:
the present disclosure provides a nonlinear texture-oriented intra prediction method, comprising:
determining a current intra prediction mode;
predicting a non-linear texture in the intra prediction module using predictive modeling comprising a quadratic function;
a reference pixel is derived from the result of predicting the non-linear texture and a predicted pixel value is generated from the position of the reference pixel.
Further, the intra prediction modes specifically include:
a normal prediction mode close to the reference pixel and an extended prediction mode far from the reference pixel.
Further, the prediction is modeled as a model using a quadratic function or a model using a linear combination of a first and a second function.
Further, predicting a nonlinear texture in the intra prediction module using predictive modeling including a quadratic function specifically includes:
the predictive modeling including quadratic functions is represented using two angular prediction modes to predict nonlinear textures.
Further, the two angular prediction modes belong to either a set of vertical prediction modes or a set of horizontal prediction modes.
Further, the deriving the reference pixel according to the result of predicting the nonlinear texture specifically includes:
when the two angular prediction modes belong to the same set of vertical prediction modes,
using the formula
Determining a reference pixel position c;
wherein, the angle alpha corresponds to the tangential direction when the quadratic function enters the prediction block, and the angle beta corresponds to the tangential direction when the quadratic function leaves the prediction block;
x 0 representing the abscissa, y 0 Indicating the ordinate, h indicating the height of the intra prediction block;
using the formula
p[x 0 ][y 0 ]＝f[0]*p[c][0]+f[1]*p[c][1]+f[2]*p[c][2]+f[3]*p[c][3]
Determining a location of an intra-predicted pixel;
wherein p [ x0] [ y0] represents an intra-prediction pixel;
when the two angular prediction modes belong together to a set of horizontal prediction modes,
using the formula
Determining a reference pixel position c;
wherein, the angle alpha corresponds to the tangential direction when the quadratic function enters the prediction block, and the angle beta corresponds to the tangential direction when the quadratic function leaves the prediction block;
x 0 representing the abscissa, y 0 Representing the ordinate, w representing the width of the intra prediction block;
using the formula
p[x 0 ][y 0 ]＝f[0]*p[0][c]+f[1]*p[0][c+1]+f[2]*p[0][c+2]+f[3]*p[0][c+3]
Determining a location of an intra-predicted pixel;
where p [ x0] [ y0] represents an intra-predicted pixel.
Further, the generating a predicted pixel value according to the position of the reference pixel specifically adopts a gaussian interpolation or a cubic spline interpolation method to generate the predicted pixel value according to the position of the reference pixel.
Further, the combination method of the angle prediction modes is based on a model of a statistical rule and is also based on a data driving method of deep learning or machine learning.
The intra prediction method for nonlinear texture provided by the application is shown in fig. 3. The present application uses two angular prediction modes to represent the nonlinear texture by means of a form of quadratic function. In fig. 3, both angle prediction modes belong to the vertical mode set. Specifically, the prediction mode close to the reference pixel is called a normal prediction mode, and the angle alpha corresponds to the tangential direction when the quadratic function enters the prediction block; the prediction mode away from the reference pixel is called extended prediction mode, and its angle β corresponds to the tangential direction when the quadratic function leaves the prediction block. At points on the same quadratic function, the pixel intensities are the same. Thus, for the predicted pixel p [ x0] [ y0], its corresponding reference pixel position c can be derived from the following equation, and its pixel intensity can be interpolated by a multi-tap filter.
When both angular prediction modes belong to the vertical mode set, the position of the reference pixel and the pixel intensity derivation process are as follows.
p[x 0 ][y 0 ]＝f[0]*p[c][0]+f[1]*p[c][1]+f[2]*p[c][2]+f[3]*p[c][3] (2)
Wherein, the angle alpha corresponds to the tangential direction when the quadratic function enters the prediction block, and the angle beta corresponds to the tangential direction when the quadratic function leaves the prediction block;
x 0 representing the abscissa, y 0 Indicating the ordinate, h indicating the height of the intra prediction block;
accordingly, when both the angle prediction modes belong to the horizontal mode set, the position of the reference pixel and the pixel intensity derivation process are shown as follows.
p[x 0 ][y 0 ]＝f[0]*p[0][c]+f[1]*p[0][c+1]+f[2]*p[0][c+2]+f[3]*p[0][c+3] (4)
In this disclosure, two angular prediction modes should belong to either the vertical mode set or the horizontal mode set. To reduce the number of searches, the conventional prediction mode can only be selected from the set of most probable modes (Most Probable Modes, MPM). Further, for each conventional prediction mode, only the 4 extended prediction modes with the highest occurrence frequency based on the statistical rule are searched. The application can obtain average coding performance gain of 0.1% in VTM10.0 Intra coding mode (All Intra, AI).
Table 1 the present disclosure is based on VTM10.0 coding performance
Embodiment two:
the present disclosure also provides a computer storage medium having stored thereon a computer program for implementing the steps of the nonlinear texture oriented intra prediction method described above when executed by a processor.
The computer storage media of the present disclosure may be implemented using semiconductor memory, magnetic core memory, drum memory, or magnetic disk memory.
Semiconductor memory devices mainly used for computers mainly include two types, mos and bipolar. The Mos device has high integration level, simple process and slower speed. Bipolar devices have complex processes, high power consumption, low integration, and high speed. After the advent of NMos and CMos, mos memories began to dominate semiconductor memories. NMos is fast, e.g., 1K bit SRAM access time from Intel corporation is 45ns. And the CMos has low power consumption, and the access time of the CMos static memory with 4K bits is 300ns. The semiconductor memories are all Random Access Memories (RAM), i.e. new contents can be read and written randomly during operation. While semiconductor read-only memory (ROM) is randomly readable but not writable during operation and is used to store cured programs and data. ROM is in turn divided into two types, non-rewritable fuse read-only memory-PROM and rewritable read-only memory EPROM.
The magnetic core memory has the characteristics of low cost and high reliability, and has practical use experience of more than 20 years. Core memory has been widely used as main memory before the mid-70 s. Its storage capacity can be up to above 10 bits, and its access time is up to 300ns. The internationally typical core memory capacity is 4 MS-8 MB with access cycles of 1.0-1.5 mus. After the rapid development of semiconductor memory replaces the location of core memory as main memory, core memory can still be applied as mass expansion memory.
A magnetic drum memory, an external memory for magnetic recording. Because of its fast information access speed, it works stably and reliably, and although its capacity is smaller, it is gradually replaced by disk memory, but it is still used as external memory for real-time process control computers and middle and large-sized computers. In order to meet the demands of small-sized and microcomputer, a microminiature magnetic drum has appeared, which has small volume, light weight, high reliability and convenient use.
A magnetic disk memory, an external memory for magnetic recording. It has the advantages of both drum and tape storage, i.e. its storage capacity is greater than that of drum, and its access speed is faster than that of tape storage, and it can be stored off-line, so that magnetic disk is widely used as external memory with large capacity in various computer systems. Magnetic disks are generally classified into hard disks and floppy disk storage.
Hard disk memory is of a wide variety. Structurally, the device is divided into a replaceable type and a fixed type. The replaceable disk platter is replaceable, and the fixed disk platter is fixed. The replaceable and fixed magnetic disks have two types of multi-disc combination and single-disc structure, and can be divided into fixed magnetic head type and movable magnetic head type. The fixed head type magnetic disk has a small capacity, a low recording density, a high access speed, and a high cost. The movable magnetic head type magnetic disk has high recording density (up to 1000-6250 bit/inch) and thus large capacity, but has low access speed compared with the fixed magnetic head magnetic disk. The storage capacity of the disk product may be up to several hundred megabytes with a bit density of 6 bits per inch and a track density of 475 tracks per inch. The disk group of the disk memory can be replaced, so that the disk memory has large capacity, large capacity and high speed, can store large-capacity information data, and is widely applied to an online information retrieval system and a database management system.
Embodiment III:
the present disclosure also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the non-linear texture oriented intra prediction method described above when the computer program is executed by the processor.
Fig. 4 is a schematic diagram of an internal structure of an electronic device in one embodiment. As shown in fig. 4, the electronic device includes a processor, a storage medium, a memory, and a network interface connected by a system bus. The storage medium of the computer device stores an operating system, a database and computer readable instructions, the database can store a control information sequence, and when the computer readable instructions are executed by a processor, the processor can realize a nonlinear texture-oriented intra-frame prediction method. The processor of the electrical device is used to provide computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may have stored therein computer readable instructions that, when executed by the processor, cause the processor to perform a non-linear texture oriented intra prediction method. The network interface of the computer device is for communicating with a terminal connection. It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The electronic device includes, but is not limited to, a smart phone, a computer, a tablet computer, a wearable smart device, an artificial smart device, a mobile power supply, and the like.
The processor may in some embodiments be comprised of integrated circuits, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory (for example, executing remote data read-write programs, etc.), and calling data stored in the memory.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory and at least one processor or the like.
Fig. 4 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 4 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Further, the electronic device may also include a network interface, optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the electronic device may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (6)
1. An intra-frame prediction method facing nonlinear texture, which is used in an intra-frame prediction module in the field of image and video coding, is characterized by comprising the following steps:
determining a current intra prediction mode;
predicting a non-linear texture in the intra prediction module using predictive modeling comprising a quadratic function;
deriving a position of a reference pixel according to a result of predicting the nonlinear texture, and generating a predicted pixel value according to interpolation of the reference pixel;
the deriving the reference pixel from the result of predicting the nonlinear texture specifically includes:
when two angular prediction modes belong to the same set of vertical prediction modes,
using the formula
Determining a reference pixel position c;
wherein, the angle alpha corresponds to the tangential direction when the quadratic function enters the prediction block, and the angle beta corresponds to the tangential direction when the quadratic function leaves the prediction block;
x 0 representing the abscissa, y 0 Indicating the ordinate, h indicating the height of the intra prediction block;
using the formula
p[x 0 ][y 0 ]＝[0]*[c][0]+[1]*[c][1]+[2]*[c][2]+[3]*p[][3]Determining a location of an intra-predicted pixel;
wherein p [ x0] [0] represents an intra-prediction pixel;
when the two angular prediction modes belong together to a set of horizontal prediction modes,
using the formula
Determining a reference pixel position c;
wherein, the angle alpha corresponds to the tangential direction when the quadratic function enters the prediction block, and the angle beta corresponds to the tangential direction when the quadratic function leaves the prediction block;
x 0 representing the abscissa, y 0 Representing the ordinate, w representing the width of the intra prediction block;
using the formula
p[x 0 ][y 0 ]＝[0]*[0][c]+[1]*[0][c+1]+[2]*[0][c+2]+[3]
*p[0][+3]
Determining a location of an intra-predicted pixel;
where p [ x0] [0] represents an intra-predicted pixel.
2. The method according to claim 1, wherein the intra prediction mode specifically comprises:
a normal prediction mode close to the reference pixel and an extended prediction mode far from the reference pixel.
3. The method according to claim 1, wherein the generating the predicted pixel value from the position of the reference pixel is performed by using a gaussian interpolation or a cubic spline interpolation method.
4. A method according to claim 3, characterized in that the combined method of angular prediction modes is based on a model of statistical law, and also on a data-driven method of deep learning or machine learning.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps corresponding to the non-linear texture oriented intra prediction method as claimed in any one of claims 1 to 4 when the computer program is executed by the processor.
6. A computer storage medium having stored thereon computer program instructions for implementing the steps corresponding to the non-linear texture oriented intra prediction method of any one of claims 1 to 4 when executed by a processor.
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