Image processing method, electronic device, and computer storage medium

Abstract

The present disclosure provides an image processing method, which comprises: dividing a still region and a suspected motion region from a to-be-processed image frame; determining motion vector information of each pixel in the suspected motion region, and dividing the each pixel into a motion pixel and a still pixel according to the motion vector information of the each pixel; marking all pixels in the still region and the still pixel as a still state, and marking the motion pixel as a motion state and corresponding motion vector information; and performing video coding processing on the marked to-be-processed image frame. The time length of motion estimation is shortened, the efficiency of image processing is improved, bandwidth resources are saved, and the pressure of video transmission is relieved. The present disclosure also provides an electronic device and a computer storage medium.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, specifically to an image processing method, an electronic device, and a computer storage medium. Background Technology

[0002] Motion estimation is a widely used technique in video encoding / decoding and video processing (such as deinterleaving). In traditional video encoding / decoding techniques, motion estimation is usually based on dividing prediction units (PUs). However, PU division is often a crude process of directly segmenting based on positional information. Therefore, low accuracy in PU motion estimation is inevitable. Furthermore, traditional video encoding / decoding techniques typically employ global motion estimation, which is not only time-consuming but also requires significant bandwidth. With the continuous improvement of video quality and resolution, the bandwidth requirements are even greater. Summary of the Invention

[0003] This disclosure addresses the aforementioned deficiencies in the prior art by providing an image processing method, an electronic device, and a computer storage medium.

[0004] In a first aspect, embodiments of this disclosure provide an image processing method, the method comprising:

[0005] Separate static regions and suspected motion regions from the image frames to be processed;

[0006] The motion vector information of each pixel in the suspected motion region is determined, and the pixels are divided into moving pixels and stationary pixels based on the motion vector information of each pixel.

[0007] The stationary pixels and all pixels in the stationary region are marked as being in a stationary state, and the moving pixels are marked as being in a moving state and as having the corresponding motion vector information.

[0008] The marked image frames to be processed are then subjected to video encoding and decoding.

[0009] In some embodiments, determining the motion vector information of each pixel in the suspected motion region includes:

[0010] The suspected motion region is divided into multiple non-overlapping macroblocks;

[0011] For each macroblock, the matching block of the current macroblock is determined from the reference frame corresponding to the current macroblock;

[0012] Based on each macroblock and its matching block, the motion vector information of all pixels in each macroblock is determined.

[0013] In some embodiments, determining the motion vector information of all pixels in each macroblock based on each macroblock and the matching block of each macroblock includes:

[0014] For each macroblock, the geometric coordinate difference between the center point of the matching block and the center point of the current macroblock is determined, and used as the motion vector information of all pixels in the current macroblock.

[0015] In some embodiments, dividing the pixels into moving pixels and stationary pixels based on the motion vector information of each pixel includes:

[0016] The pixels that meet the preset conditions are identified as stationary pixels, and all pixels other than the stationary pixels are identified as moving pixels. The preset conditions include: the motion vector information is zero, and the frame difference between the image frame to be processed and the reference frame is less than a preset threshold.

[0017] In some embodiments, the static region includes a background region and a static target region, and the suspected motion region includes a moving target region; the step of dividing the static region and the suspected motion region from the image frame to be processed includes:

[0018] The image frame to be processed is divided into a foreground region and a background region;

[0019] Identify the targets in each of the aforementioned foreground regions;

[0020] Based on the target in each of the foreground regions, each of the foreground regions is divided into a stationary target region and a moving target region.

[0021] In some embodiments, dividing each of the foreground regions into a stationary target region and a moving target region based on the target in each of the foreground regions includes:

[0022] For any target in any of the foreground regions, when motion is detected in the current target, a preset range area centered on the current target in the current foreground region is determined as the moving target region;

[0023] All regions in each of the foreground regions, except for the moving target region, are defined as the stationary target regions.

[0024] In some embodiments, the number of image frames to be processed is multiple; the step of dividing the image frames to be processed into stationary regions and suspected motion regions includes:

[0025] Determine the frame difference between each of the image frames to be processed and the corresponding reference frames;

[0026] Based on the frame differences, each of the image frames to be processed is divided into the static region and the suspected motion region.

[0027] In some embodiments, dividing each of the image frames to be processed into the stationary region and the suspected motion region based on the frame differences includes:

[0028] Image frames with a frame difference greater than or equal to a preset motion / static discrimination threshold are identified as suspected motion regions, while image frames with a frame difference less than the preset motion / static discrimination threshold are identified as stationary regions.

[0029] By dividing the image frame to be processed into static regions and suspected motion regions, and performing local motion estimation only on the suspected motion regions, the motion vector information of each pixel in the suspected motion regions can be determined. Based on the motion vector information of each pixel, each pixel is divided into moving pixels and static pixels. The static pixels and all pixels in the static regions are marked as static, and the moving pixels are marked as moving and with the corresponding motion vector information. The marked image frame to be processed is then subjected to video encoding and decoding processing without the need for global motion estimation of the image frame to be processed. This shortens the motion estimation time and improves the efficiency of image processing. Furthermore, the identified static pixels (including pixels in the static regions) have a smaller bandwidth requirement when performing video encoding and decoding processing, while the moving pixels have a larger bandwidth requirement. Targeted processing of static and moving pixels can also save bandwidth resources and alleviate the pressure on video transmission. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the image processing method provided in the embodiments of this disclosure. Figure One ;

[0031] Figure 2 This is a flowchart illustrating the image processing method provided in the embodiments of this disclosure. Figure Two ;

[0032] Figure 3 This is a block matching diagram provided in an embodiment of the present disclosure;

[0033] Figure 4 This is a flowchart illustrating the image processing method provided in the embodiments of this disclosure. Figure Three ;

[0034] Figure 5 This is a flowchart illustrating the image processing method provided in the embodiments of this disclosure. Figure Four ;

[0035] Figure 6This is a flowchart illustrating the image processing method provided in the embodiments of this disclosure. Figure Five . Detailed Implementation

[0036] Exemplary embodiments will be described more fully below with reference to the accompanying drawings; however, these exemplary embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will enable those skilled in the art to fully understand the scope of this disclosure.

[0037] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0038] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the said feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded.

[0039] The embodiments described herein can be described with reference to plan views and / or cross-sectional views using the ideal schematic diagrams of this disclosure. Therefore, the example illustrations can be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to those shown in the drawings, but include modifications to configurations formed based on manufacturing processes. Therefore, the areas illustrated in the drawings are schematic in nature, and the shapes of the areas shown in the figures illustrate specific shapes of areas of an element, but are not intended to be limiting.

[0040] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0041] Traditional video encoding and decoding technologies typically rely on partitioning the Puzzle Unit (PU) for motion estimation and perform global motion estimation on image frames. This results in low accuracy, long processing time, and high bandwidth requirements. Therefore, this disclosure proposes that for some local motion scenarios (such as live streaming scenarios), these scenarios share a common characteristic: most areas are actually stationary, with only a small portion in motion. Thus, stationary areas and suspected motion areas can be initially detected. Further motion detection can then be performed in the suspected motion areas, and local motion estimation can be conducted to determine the motion vector information of pixels within these areas. The suspected motion areas can then be further divided into stationary and moving pixels, and motion state markers can be assigned to them. Video encoding and decoding can then be performed directly based on these motion state markers.

[0042] like Figure 1 As shown in the figure, this disclosure provides an image processing method, which may include the following steps:

[0043] In step S11, stationary regions and suspected motion regions are divided from the image frame to be processed;

[0044] In step S12, the motion vector information of each pixel in the suspected motion region is determined, and the pixels are divided into moving pixels and stationary pixels according to the motion vector information of each pixel;

[0045] In step S13, the stationary pixels and all pixels in the stationary region are marked as being in a stationary state, and the moving pixels are marked as being in a moving state and as being marked with the corresponding motion vector information.

[0046] In step S14, the marked image frame to be processed is subjected to video encoding and decoding processing.

[0047] Both the stationary region and the suspected motion region include multiple pixels. The stationary region refers to the area where the pixels do not move, while the suspected motion region refers to the area where the pixels are suspected of moving.

[0048] Separating stationary regions and suspected moving regions from the image frame to be processed is a motion detection process, which can be performed by any traditional image processing operation or deep learning neural network capable of motion detection, such as image segmentation networks, MSE (Mean Square Error) operation, MAE (Mean Absolute Error) operation, SAD (Sum of Absolute Difference) operation, frame difference calculation, etc.

[0049] Determining the motion vector information of each pixel in the suspected motion region and dividing each pixel into moving pixels and stationary pixels based on the motion vector information is a local motion estimation process. It can be performed by any traditional image processing operation or deep learning neural network capable of motion estimation, such as image patch matching, optical flow, optical flow network, etc.

[0050] As can be seen from steps S11-S14 above, the image processing method provided in this embodiment divides the image frame to be processed into a static region and a suspected motion region. It performs local motion estimation only on the suspected motion region to determine the motion vector information of each pixel in the suspected motion region. Based on the motion vector information of each pixel, the pixels are divided into moving pixels and static pixels. The static pixels and all pixels in the static region are marked as static. The moving pixels are marked as moving and the corresponding motion vector information. The marked image frame to be processed is then subjected to video encoding and decoding processing. This eliminates the need for global motion estimation of the image frame to be processed, shortening the motion estimation time and improving the efficiency of image processing. Furthermore, the identified static pixels (including pixels in the static region) have a smaller bandwidth requirement when performing video encoding and decoding processing, while the moving pixels have a larger bandwidth requirement. Targeted processing of static and moving pixels can also save bandwidth resources and alleviate the pressure on video transmission.

[0051] Motion estimation can be performed only on suspected motion regions using methods such as image patch matching, optical flow, and optical flow networks. Among these, image patch matching is convenient, fast, and highly accurate. Correspondingly, in some embodiments, such as... Figure 2 As shown, determining the motion vector information of each pixel in the suspected motion region (i.e., as described in step S12) may include the following steps:

[0052] In step S121, the suspected motion region is divided into multiple non-overlapping macroblocks;

[0053] In step S122, for each macroblock, the matching block of the current macroblock is determined from the reference frame corresponding to the current macroblock;

[0054] In step S123, motion vector information of all pixels in each macroblock is determined based on each macroblock and its matching block.

[0055] A macroblock typically consists of a luminance pixel block and two additional chrominance pixel blocks. The reference frame corresponding to the macroblock refers to the reference frame of the image frame in which the macroblock is located. In this field, the type and number of reference frames are related to the type of the current frame. For example, when the current frame is a P frame, the reference frame is the I frame or P frame preceding the current frame. When the current frame is a B frame, the reference frame is the I frame and / or the P frame preceding and / or following the current frame. This will not be elaborated further in the embodiments of this disclosure.

[0056] First, the suspected motion region is divided into multiple non-overlapping macroblocks, assuming that all pixels within each macroblock have the same motion vector information. Further, for each macroblock, the most similar block is searched from the reference frame; this is called the matching block. The similarity calculation and determination of the most similar block can be performed using the SAD algorithm, which is simple and fast. Finally, for each macroblock, the motion vector information corresponding to that macroblock, i.e., the motion vector information of all pixels within that macroblock, can be determined based on the macroblock and its matching block.

[0057] For example, such as Figure 3 The diagram illustrates a block matching method provided in this embodiment. Taking a macroblock (referred to as the current block) in a suspected motion region as an example, the center point of the current block is used as the center point in the reference frame (i.e., point (x, y) shown in the diagram). Within a search region near this center point, the most similar matching block is searched. The center point of the matching block is (x1, y1). The difference in geometric coordinates between the center point of the current block and the center point of the matching block can be used as the motion vector from the current block to the matching block, or as the motion vector of all pixels in the current block.

[0058] Accordingly, in some embodiments, determining the motion vector information of all pixels in each macroblock based on each macroblock and the matching block of each macroblock (i.e., step S123) may include the following steps: for each macroblock, determining the geometric coordinate difference between the center point of the matching block of the current macroblock and the center point of the current macroblock, as the motion vector information of all pixels in the current macroblock.

[0059] For example, if the center point of the matching block of the current macroblock is (x1, y1) and the center point of the current macroblock is (x, y), then calculate the difference in geometric coordinates between (x1, y1) and (x, y). mv can be used as the motion vector information for all pixels in the current macroblock.

[0060] When the motion vector is not zero, it indicates that the pixel must have moved. However, when the motion vector is zero, it is not sufficient to indicate that the pixel must not have moved. Further judgment is needed by considering the frame difference between the image frame to be processed and the reference frame. Accordingly, in some embodiments, dividing the pixels into moving pixels and stationary pixels based on the motion vector information of each pixel (i.e., as described in step S12) includes: determining the pixels that meet the preset conditions as stationary pixels, and determining all pixels other than the stationary pixels as moving pixels. The preset conditions include: the motion vector information is zero, and the frame difference between the image frame to be processed and the reference frame is less than a preset threshold.

[0061] In other words, pixels in the suspected motion region whose motion vector information is zero and whose corresponding frame difference is less than a preset threshold can be identified as stationary pixels, while pixels in the suspected motion region whose motion vector information is zero and whose corresponding frame difference is greater than or equal to a preset threshold, as well as pixels whose motion vector is not zero (regardless of whether the corresponding frame difference is zero) can be identified as moving pixels.

[0062] The frame difference between the image frame to be processed and its reference frame refers to the average difference between each pixel in the image frame to be processed and each pixel in the reference frame, i.e., the average pixel difference. When the motion vector information is zero and the frame difference between the image frame to be processed and its reference frame is less than a preset threshold, the pixel can be reasonably considered to be stationary and not moving.

[0063] To separate static and potentially moving regions from the image frames to be processed, one can use an image segmentation algorithm to segment each image frame into static and potentially moving regions, or one can directly classify multiple image frames by performing motion pre-detection and determine each image frame as a static or potentially moving region.

[0064] Accordingly, in some embodiments, the stationary region includes a background region and a stationary target region, and the suspected moving region includes a moving target region; such as Figure 4 As shown, the step of dividing the static region and the suspected motion region from the image frame to be processed (i.e., step S11) may include the following steps:

[0065] In step S111, the image frame to be processed is segmented into a foreground region and a background region;

[0066] In step S112, targets in each of the foreground regions are identified;

[0067] In step S113, each of the foreground regions is divided into a stationary target region and a moving target region according to the target in each of the foreground regions.

[0068] The segmentation of the image frame to be processed into foreground and background regions, and the identification of targets within each foreground region, can be performed using any conventional image processing operation or deep learning neural network capable of image segmentation. For example, it can be done using FCN (Full Connected Network), SegNet (Segmentation Network), U-Net (U-shape Network), etc. In this art, the foreground region typically refers to a region containing local motion, and the target typically refers to the main subject in the image, such as a person, animal, plant, etc., which will not be elaborated further in this disclosure.

[0069] After dividing each foreground region into a stationary target region and a moving target region, both the stationary target region and the background region are directly treated as stationary regions, assuming that the pixels within these regions do not move and therefore motion estimation is not required. The moving target region, however, is treated as a suspected moving region and requires motion estimation to further determine whether the pixels within it are moving.

[0070] After identifying targets in each foreground region, it is possible to further detect whether the targets are moving. Accordingly, in some embodiments, such as Figure 5 As shown, dividing each of the foreground regions into a stationary target region and a moving target region based on the target in each of the foreground regions (i.e., step S113) may include the following steps:

[0071] In step S1131, for any target in any foreground region, when motion is detected in the current target, a preset range area centered on the current target in the current foreground region is determined as the moving target region;

[0072] In step S1132, all regions in each of the foreground regions except for the moving target region are determined as the stationary target region.

[0073] Detecting whether a target is moving can be done using simple image processing methods, such as comparing the change in the target's geometric position between consecutive frames. Consecutive frames refer to the frame before and after the target in the image being processed. A predefined area centered on the target must include at least the entire target. For detected moving targets, the predefined area centered on that target is designated as the moving target region. These moving target regions can overlap. After identifying all moving target regions, the area remaining is designated as the stationary target region.

[0074] The reason why all moving target areas are identified before all other areas are considered stationary target areas, instead of considering a preset range centered on a non-moving target as a stationary target area when no movement is detected, is that if targets are detected sequentially and the preset range centered on a moving target is considered a moving target area, and the preset range centered on a non-moving target is considered a stationary target area, it is very likely that the later identified stationary target areas will cover the previously identified moving target areas. In other words, moving target areas will be misidentified as stationary target areas. Therefore, to avoid moving target areas being misidentified as stationary target areas and thus reduce the risk of misidentification and improve recognition accuracy, all moving target areas are identified before all other areas are considered stationary target areas.

[0075] Besides using image segmentation algorithms to segment static and potentially moving regions from each image frame, multiple image frames can also be directly classified by performing motion pre-detection. Motion pre-detection can employ traditional image processing operations such as calculating frame differences. Accordingly, in some embodiments, the number of image frames to be processed is multiple; such as... Figure 6 As shown, the step of dividing the static region and the suspected motion region from the image frame to be processed (i.e., step S11) may include the following steps:

[0076] In step S111', the frame difference between each of the image frames to be processed and the corresponding reference frame is determined;

[0077] In step S112', each of the image frames to be processed is divided into the static region and the suspected motion region according to the frame difference.

[0078] The frame difference between the image frame to be processed and the corresponding reference frame refers to the average difference between each pixel in the image frame to be processed and each pixel in the reference frame, i.e., the average pixel difference. The difference between the current image frame to be processed and the reference frame can be expressed as: frame_diff = |frame(t) - frame(t-1)|, where frame(t) represents the current image frame to be processed, frame(t-1) represents the reference frame of the current image frame to be processed, and frame_diff represents the frame difference.

[0079] When the frame difference is sufficiently small, it indicates that the difference between the current image frame to be processed and the reference frame is small, and it can be reasonably assumed that the current image frame to be processed belongs to a static region, that is, the pixels in the current image frame to be processed do not move. Accordingly, in some embodiments, the step of dividing each image frame to be processed into the static region and the suspected motion region according to the frame difference (i.e., step S112') may include the following steps: determining the image frames to be processed with a frame difference greater than or equal to a preset motion / static discrimination threshold as the suspected motion region, and determining the image frames to be processed with a frame difference less than the preset motion / static discrimination threshold as the static region.

[0080] If the preset motion / static discrimination threshold is denoted as threshold, then a frame difference greater than or equal to the preset motion / static discrimination threshold can be expressed as: frame_diff>=threshold, and a frame difference less than the preset motion / static discrimination threshold can be expressed as: frame_diff<threshold.

[0081] Furthermore, embodiments of this disclosure also provide an electronic device, including:

[0082] One or more processors;

[0083] A storage device on which one or more programs are stored;

[0084] When the one or more programs are executed by the one or more processors, the one or more processors implement the image processing method as described above.

[0085] Furthermore, this disclosure also provides a computer storage medium storing a computer program thereon, wherein the program, when executed, implements the image processing method as described above.

[0086] It will be understood by those skilled in the art that all or some of the steps in the methods disclosed above, and the functional modules / units in the apparatus, can be implemented as software, firmware, hardware, and suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0087] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in connection with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in connection with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. An image processing method, characterized in that, The method includes: Separate static regions and suspected motion regions from the image frames to be processed; The motion vector information of each pixel in the suspected motion region is determined, and the pixels are divided into moving pixels and stationary pixels based on the motion vector information of each pixel. The stationary pixels and all pixels in the stationary region are marked as being in a stationary state, and the moving pixels are marked as being in a moving state and as having the corresponding motion vector information. The marked image frames to be processed are then subjected to video encoding and decoding. The static region includes a background region and a static target region, and the suspected motion region includes a moving target region; the process of dividing the static region and the suspected motion region from the image frame to be processed includes: The image frame to be processed is divided into a foreground region and a background region; Identify the targets in each of the aforementioned foreground regions; Based on the target in each of the foreground regions, each of the foreground regions is divided into a stationary target region and a moving target region; Based on the motion vector information of each pixel, the pixels are divided into moving pixels and stationary pixels, including: The pixels that meet the preset conditions are identified as stationary pixels, and all pixels other than the stationary pixels are identified as moving pixels. The preset conditions include: the motion vector information is zero, and the frame difference between the image frame to be processed and the reference frame is less than a preset threshold.

2. The method according to claim 1, characterized in that, The motion vector information of each pixel in the suspected motion region is determined as follows: The suspected motion region is divided into multiple non-overlapping macroblocks; For each macroblock, the matching block of the current macroblock is determined from the reference frame corresponding to the current macroblock; Based on each macroblock and its matching block, the motion vector information of all pixels in each macroblock is determined.

3. The method according to claim 2, characterized in that, The step of determining the motion vector information of all pixels in each macroblock based on each macroblock and the matching block of each macroblock includes: For each macroblock, the geometric coordinate difference between the center point of the matching block and the center point of the current macroblock is determined, and used as the motion vector information of all pixels in the current macroblock.

4. The method according to claim 1, characterized in that, The step of dividing each of the foreground regions into a stationary target region and a moving target region based on the targets in each of the foreground regions includes: For any target in any of the foreground regions, when motion is detected in the current target, a preset range area centered on the current target in the current foreground region is determined as the moving target region; All regions in each of the foreground regions, except for the moving target region, are defined as the stationary target regions.

5. The method according to any one of claims 1-3, characterized in that, The number of image frames to be processed is multiple; the process of dividing the image frames to be processed into static regions and suspected motion regions includes: Determine the frame difference between each of the image frames to be processed and the corresponding reference frames; Based on the frame differences, each of the image frames to be processed is divided into the static region and the suspected motion region.

6. The method according to claim 5, characterized in that, The step of dividing each of the image frames to be processed into the static region and the suspected motion region according to the frame difference includes: Image frames with a frame difference greater than or equal to a preset motion / static discrimination threshold are identified as suspected motion regions, while image frames with a frame difference less than the preset motion / static discrimination threshold are identified as stationary regions.

7. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the image processing method as described in any one of claims 1-6.

8. A computer storage medium having a computer program stored thereon, wherein, When the program is executed, it implements the image processing method as described in any one of claims 1-6.

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