CN111800633B - Image processing method and device - Google Patents
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Abstract
The disclosure provides an image processing method and device, relates to the field of image processing, and can solve the problem that image compression cannot reduce the data volume in the image transmission process to the greatest extent. The specific technical scheme is as follows: obtaining a frame image to be processed, and carrying out macro block division on the frame image to be processed to generate at least one macro block; preprocessing each macro block in the at least one macro block to obtain a preprocessed macro block corresponding to each macro block; layering each preprocessing macro block according to a preset layering rule to obtain layering data corresponding to each preprocessing macro block; extracting layered data corresponding to each preprocessing macro block according to a preset data extraction rule to obtain target data corresponding to each preprocessing macro block; and transmitting the target data corresponding to each preprocessing macro block to an image receiving device. The invention is used for reducing the data volume in the image transmission process.
Description
Technical Field
The present disclosure relates to the field of image processing, and in particular, to an image processing method and apparatus.
Background
With the development of information technology, the requirements of people on communication services are increasing, and more images, especially video images, are transmitted in the communication process. In the transmission process of video images, the data volume is huge, and if image compression is not performed, video image transmission and storage are difficult. Thus, image compression capability is particularly important for video images.
Image compression is the application of data compression techniques to digital images in order to reduce redundant information in the image data, thereby enabling more efficient storage and transmission of the image in a format.
However, the current image compression is usually to compress the whole data of the image, and this way cannot reduce the data amount in the image transmission process to the greatest extent.
Disclosure of Invention
The embodiment of the disclosure provides an image processing method and device, which can solve the problem that the data volume in the image transmission process cannot be reduced to the greatest extent by the current image compression. The technical scheme is as follows:
According to a first aspect of embodiments of the present disclosure, there is provided an image processing method applied to an image transmission apparatus, the method including:
obtaining a frame image to be processed, and carrying out macro block division on the frame image to be processed to generate at least one macro block;
Preprocessing each macro block in the at least one macro block to obtain a preprocessed macro block corresponding to each macro block, wherein each preprocessed macro block comprises a Direct Current (DC) component and at least one Alternating Current (AC) component;
Layering each pre-processed macro block according to a preset layering rule to obtain layering data corresponding to each pre-processed macro block, wherein the layering data comprise a base layer and at least one enhancement layer, the DC component is the base layer, the AC component is divided into at least one enhancement layer according to the importance degree, and the importance degree of the AC component in the same enhancement layer is the same;
extracting layered data corresponding to each pre-processed macro block according to a preset data extraction rule to obtain target data corresponding to each pre-processed macro block, wherein the target data comprises a basic layer data and at least one enhancement layer data;
And transmitting the target data corresponding to each preprocessing macro block to an image receiving device.
The image processing method provided by the embodiment of the disclosure can acquire a frame image to be processed, and macro block division is performed on the frame image to be processed to generate at least one macro block; preprocessing each macro block in the at least one macro block to obtain a preprocessed macro block corresponding to each macro block, wherein each preprocessed macro block comprises a DC component and at least one AC component; extracting layered data corresponding to each pre-processed macro block according to a preset data extraction rule to obtain target data corresponding to each pre-processed macro block, wherein the target data comprises a basic layer data and at least one enhancement layer data; and transmitting the target data corresponding to each preprocessing macro block to image receiving equipment, wherein in the image transmission process, only the target data corresponding to each preprocessing macro block is transmitted, the transmitted data volume is less, and the problem that the data volume in the image transmission process cannot be reduced to the greatest extent in the current image compression is solved.
In one embodiment, the target data includes one base layer data and three enhancement layer data, the three enhancement layer data being the most important three enhancement layer data in the AC component.
The target data comprises a basic layer data and three enhancement layer data, only one basic layer data and three enhancement layer data corresponding to each preprocessing macro block are required to be sent to the image receiving equipment, in the image transmission process, only the target data corresponding to each preprocessing macro block is sent, the transmitted data volume is small, and the problem that the data volume in the image transmission process cannot be reduced to the greatest extent in the current image compression data is solved.
In one embodiment, the preprocessing each of the at least one macroblock comprises:
Performing DCT on each macro block in the at least one macro block to obtain a transformed macro block corresponding to each macro block, wherein the transformed macro block comprises a DC component and at least one AC component;
and carrying out quantization processing on each transformed macroblock to obtain a quantized macroblock corresponding to each transformed macroblock, wherein the quantized macroblock comprises a quantized DC component and at least one quantized AC component.
By performing the DCT transform and quantization processing on each macroblock of at least one macroblock, the data processing amount can be effectively reduced.
In one embodiment, before the sending the target data corresponding to each of the preprocessed macro blocks to the image receiving device, the method further includes:
And encoding the target data corresponding to each preprocessing macro block to obtain an encoded macro block corresponding to each preprocessing macro block.
By encoding the target data corresponding to each pre-processed macro block, the safety of the target data in the transmission process can be improved.
According to a second aspect of embodiments of the present disclosure, there is provided an image processing method applied to an image receiving apparatus, the method including:
receiving target data of each macro block sent by image sending equipment;
Performing dimension reduction on each macro block according to the gray level of the basic layer data of each macro block to obtain a dimension reduced frame image;
Amplifying the frame image after the dimension reduction into a frame image with a target dimension;
and carrying out super-resolution reconstruction on the frame image with the target size and the at least one enhancement layer data to generate a target frame image.
The image processing method provided by the embodiment of the disclosure can receive the target data of each macro block sent by the image sending equipment; reducing the dimension of each macro block according to the gray level of the basic layer data of each macro block to obtain a frame image after dimension reduction; amplifying the frame image after the dimension reduction into a frame image with a target dimension; the frame image with the target size and the at least one enhancement layer data are subjected to super-resolution reconstruction to generate a target frame image, only target data corresponding to each preprocessing macro block are received in the image transmission process, the transmitted data size is small, the problem that the data size in the image transmission process cannot be reduced to the greatest extent in the current image compression is solved, and the image processing method provided by the embodiment reduces the blocking effect of the target frame image and improves the resolution of the target frame image.
In one embodiment, the super-resolution reconstructing the frame image of the target size and the at least one enhancement layer data comprises:
the frame image of the target size and the at least one enhancement layer data are input into a pre-trained super-resolution reconstruction convolutional neural network SRCNN model.
The resolution of the target frame image can be improved by inputting the frame image of the target size and the at least one enhancement layer data into a pre-trained super-resolution reconstruction convolutional neural network SRCNN model.
In one embodiment, each macroblock sent by the image sending device is an encoded macroblock, and after receiving the target data of each macroblock sent by the image sending device, the method further includes:
and decoding each macro block to generate a decoded macro block corresponding to each macro block.
By decoding each macroblock, target data of the macroblock can be effectively restored.
According to a third aspect of the embodiments of the present disclosure, there is provided an image processing apparatus applied to an image transmission device, the apparatus including:
The frame image processing device comprises a frame image acquisition module to be processed, a frame image processing module and a frame image processing module, wherein the frame image acquisition module to be processed is used for acquiring a frame image to be processed, and carrying out macro block division on the frame image to be processed to generate at least one macro block;
a macro block preprocessing module, configured to preprocess each macro block in the at least one macro block to obtain a preprocessed macro block corresponding to each macro block, where each preprocessed macro block includes a DC component and at least one AC component;
the preprocessing macro block layering module is used for layering each preprocessing macro block according to a preset layering rule to obtain layering data corresponding to each preprocessing macro block, wherein the layering data comprise a basic layer and at least one enhancement layer, the DC component is the basic layer, the AC component is divided into at least one enhancement layer according to the importance degree, and the importance degree of the AC components in the same enhancement layer is the same;
The hierarchical data extraction module is used for extracting the hierarchical data corresponding to each preprocessing macro block according to a preset data extraction rule to obtain target data corresponding to each preprocessing macro block, wherein the target data comprises a basic layer data and at least one enhancement layer data;
and the target data sending module is used for sending the target data corresponding to each preprocessing macro block to the image receiving equipment.
In one embodiment, the target data includes one base layer data and three enhancement layer data, the three enhancement layer data being the most important three enhancement layer data in the AC component.
In one embodiment, the macroblock preprocessing module is specifically configured to:
Performing DCT on each macro block in the at least one macro block to obtain a transformed macro block corresponding to each macro block, wherein the transformed macro block comprises a DC component and at least one AC component;
and carrying out quantization processing on each transformed macroblock to obtain a quantized macroblock corresponding to each transformed macroblock, wherein the quantized macroblock comprises a quantized DC component and at least one quantized AC component.
In one embodiment, the apparatus further comprises:
and the macro block coding module is used for coding the target data corresponding to each preprocessing macro block to obtain coded macro blocks corresponding to each preprocessing macro block.
According to a fourth aspect of embodiments of the present disclosure, there is provided an image processing apparatus applied to an image receiving device, the method including:
a target data receiving module, configured to receive target data of each macro block according to the first aspect sent by the image sending device;
The macro block dimension reduction module is used for reducing the dimension of each macro block according to the gray level of the basic layer data of each macro block to obtain a frame image after dimension reduction;
the frame image amplifying module is used for amplifying the frame image subjected to the dimension reduction into a frame image with a target dimension;
and the target frame image generation module is used for carrying out super-resolution reconstruction on the frame image with the target size and the at least one enhancement layer data to generate a target frame image.
In one embodiment, the apparatus further comprises:
And the macro block decoding module is used for decoding each macro block and generating a decoded macro block corresponding to each macro block.
A fifth aspect of the disclosed embodiments provides an image processing apparatus comprising a processor and a memory, the memory having stored therein at least one computer instruction that is loaded and executed by the processor to implement the steps performed in the image processing method of any of the first aspects.
A sixth aspect of the disclosed embodiments provides a computer readable storage medium having stored therein at least one computer instruction loaded and executed by a processor to implement the steps performed in the image processing method of any of the first aspects.
A seventh aspect of the embodiments of the present disclosure provides an image processing apparatus comprising a processor and a memory, the memory having stored therein at least one computer instruction that is loaded and executed by the processor to implement the steps performed in the image processing method according to any one of the second aspects.
An eighth aspect of the embodiments of the present disclosure provides a computer-readable storage medium having stored therein at least one computer instruction that is loaded and executed by a processor to implement the steps performed in the image processing method of any of the second aspects.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a flowchart of a method of image processing provided by the disclosed embodiments;
FIG. 2 is a hierarchical rule diagram of an 8×8 macroblock as shown in an embodiment of the present disclosure;
fig. 3 is a flowchart two of an image processing method according to an embodiment of the present disclosure;
FIG. 4 is a flowchart III of an image processing method provided by an embodiment of the present disclosure;
fig. 5 is a flowchart fourth of an image processing method provided in an embodiment of the present disclosure;
FIG. 6 is a block diagram of a SRCNN model provided by an embodiment of the present disclosure;
FIG. 7 is a parameter block diagram of a SRCNN model provided by an embodiment of the present disclosure;
FIG. 8 is a flow chart of a SRCNN model training method provided by an embodiment of the present disclosure;
fig. 9 is a first block diagram of an image processing apparatus provided in an embodiment of the present disclosure;
fig. 10 is a second block diagram of an image processing apparatus provided by the disclosed embodiment;
fig. 11 is a block diagram third of an image processing apparatus provided by the disclosed embodiment;
fig. 12 is a block diagram of an image processing apparatus provided by the disclosed embodiment;
Fig. 13 is a block diagram first of an image processing apparatus provided by the disclosed embodiment;
Fig. 14 is a block diagram of an image processing apparatus according to the embodiment of the disclosure.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
Fig. 1 is a flowchart one of an image processing method according to an embodiment of the present disclosure, where the image processing method is applied to an image sending device, and the image sending device is an image encoding end. As shown in fig. 1, the image processing method includes the steps of:
S101, acquiring a frame image to be processed, and dividing macro blocks of the frame image to be processed to generate at least one macro block.
The at least one macroblock may be a 4×4 macroblock, an 8×8 macroblock, a 16×16 macroblock, or other size macroblock, for example. Preferably, in the present embodiment, the at least one macroblock is an 8×8 macroblock.
S102, preprocessing each macro block in the at least one macro block to obtain a preprocessed macro block corresponding to each macro block, wherein each preprocessed macro block comprises a Direct Current (DC) component and at least one Alternating Current (AC) component.
How each of the at least one macroblock is preprocessed is described as follows:
Illustratively, each of the at least one macroblock is first discrete cosine transformed (Discrete Cosine Transform, DCT) to obtain a transformed macroblock corresponding to each macroblock, the transformed macroblock comprising a DC component and at least one AC component;
And carrying out quantization processing on each transformed macroblock to obtain a quantized macroblock corresponding to each transformed macroblock, wherein the quantized macroblock comprises a quantized DC component and at least one quantized AC component.
Illustratively, a pre-processed macroblock obtained by pre-processing an 8×8 macroblock includes one DC component and 63 AC components.
S103, layering each preprocessed macro block according to a preset layering rule to obtain layering data corresponding to each preprocessed macro block, wherein the layering data comprise a base layer and at least one enhancement layer, the DC component is the base layer, the AC component is divided into at least one enhancement layer according to importance degrees, and the importance degrees of the AC components in the same enhancement layer are the same.
Illustratively, the preset layering rules specify the number of layers divided by each macro block and the positions of the pixel points included in each layer in the macro block. In practical applications, for an 8×8 macroblock, it may be divided into 16 layers.
Fig. 2 is a schematic hierarchical rule diagram of an 8×8 macroblock according to an embodiment of the present disclosure, referring to fig. 2, pixels belonging to the same layer are labeled with the same color, for example, a DC component is a first base layer, AC components such as AC1 and AC2 belong to a second enhancement layer, AC3 and AC4 belong to a third enhancement layer, AC5, AC6 and AC7 belong to a fourth enhancement layer, and so on, AC58, AC59, AC60, AC61, AC62 and AC63 belong to a sixteenth enhancement layer, and so on, where the importance of the DC component is highest, the importance of the AC component is sequentially from high to low, and the importance of the AC component in the same enhancement layer is the same as that of the second enhancement layer, the third enhancement layer … …, and so on.
The foregoing is merely an exemplary layering rule schematic, in practical application, the layering rule may be set and adjusted according to actual needs, and once the layering rule is determined, all macro blocks in the current frame image are layered according to the layering rule, so as to determine the pixel point included in each layer.
S104, extracting the layered data corresponding to each preprocessing macro block according to a preset data extraction rule to obtain target data corresponding to each preprocessing macro block, wherein the target data comprises a basic layer data and at least one enhancement layer data.
The preset data extraction rules require that the number of data layers extracted per pre-processed macroblock be specified. Typically, lower layer data is preferentially selected. For example, the first layer may be selected, that is, the base layer data may be transmitted, or the 1 st to N th layers may be selected, where the value of N may be set according to actual needs. According to the empirical value, the value of N is not more than 4.
In this embodiment, N is 4, that is, the target data includes one base layer data and three enhancement layer data, which are data of three enhancement layers with the highest importance in the AC component. Taking an 8×8 macroblock as an example, the target data includes first base layer data and second, third, and fourth enhancement layer data.
S105, the target data corresponding to each preprocessing macro block is sent to the image receiving equipment.
The target data corresponding to each of the preprocessed macro blocks is encoded to obtain encoded macro blocks corresponding to each of the preprocessed macro blocks, and then the encoded macro blocks corresponding to each of the preprocessed macro blocks are transmitted to the image receiving device.
The image processing method provided by the embodiment of the disclosure can acquire a frame image to be processed, and macro block division is performed on the frame image to be processed to generate at least one macro block; preprocessing each macro block in the at least one macro block to obtain a preprocessed macro block corresponding to each macro block, wherein each preprocessed macro block comprises a DC component and at least one AC component; extracting layered data corresponding to each pre-processed macro block according to a preset data extraction rule to obtain target data corresponding to each pre-processed macro block, wherein the target data comprises a basic layer data and at least one enhancement layer data; and transmitting the target data corresponding to each preprocessing macro block to image receiving equipment, wherein in the image transmission process, only the target data corresponding to each preprocessing macro block is transmitted, the transmitted data volume is less, and the problem that the data volume in the image transmission process cannot be reduced to the greatest extent in the current image compression is solved.
The image processing method shown in the embodiment of fig. 1 will be described in further detail with reference to the embodiment of fig. 3. Fig. 3 is a flowchart two of an image processing method according to an embodiment of the disclosure. As shown in fig. 3, the image processing method includes the steps of:
s301, acquiring a current frame image;
s302, preprocessing the current frame image, wherein the preprocessing comprises the following steps: DCT transformation and quantization;
Specifically, the preprocessing of the current frame image is performed in units of macro blocks, that is, the entire current frame image is divided into a plurality of macro blocks before the preprocessing is performed, and then the preprocessing is performed for each macro block, and the size of each macro block can be set according to actual needs, and in JPEG encoding, the image frame is generally divided into 8×8 macro blocks for processing.
The DCT transform can transform the spatial domain signal to the frequency domain, and has good decorrelation performance. The DCT transformation itself is lossless, but creates good conditions for subsequent quantization, encoding, etc. in the image encoding process, and at the same time, since the DCT transformation is symmetrical, the original image information can be restored at the receiving end by using the DCT inverse transformation after the quantization encoding.
The data obtained by preprocessing the current frame image mainly includes a DC component part and an AC component part, and specifically, in the case of an 8×8 macroblock, one DC component and 63 AC components will be obtained.
S303, layering the preprocessed frame image;
The layering is still performed in units of macro blocks, specifically, layering is performed for each macro block according to a preset layering rule. The preset layering rules prescribe the number of layers divided by each macro block and the positions of pixel points contained in each layer in the macro block. In practical applications, for an 8×8 macroblock, it may be divided into 16 layers.
Specifically, referring to fig. 2, pixels belonging to the same layer are labeled with the same color, for example, DC is a first base layer, AC1, AC2 are second enhancement layers, AC3, AC4 are third enhancement layers, AC5, AC6, AC7 are fourth enhancement layers, and so on, AC58, AC59, AC60, AC61, AC62, AC63 are sixteenth enhancement layers, and so on.
The foregoing is merely an exemplary layering rule schematic, in practical application, the layering rule may be set and adjusted according to actual needs, and once the layering rule is determined, all macro blocks in the current frame image are layered according to the layering rule, so as to determine the pixel point included in each layer.
The layering rules are set according to the data which are mainly preprocessed, wherein the preprocessed data generally comprise a DC component part and an AC component part, and the basic principle is that: taking the DC component as a first layer, and mainly taking charge of displaying the outline part of the whole picture; the AC components are layered according to their importance levels, with the importance levels being quite divided at the same layer. After the layering, each layer is numbered, and when the number is increased, the number of layers with high importance is increased, that is, the importance is gradually decreased from the 2 nd layer to the 16 th layer.
S304, taking out one or more layers of preset data from the current frame image;
In this step, one or more layers of data are extracted according to a preset data extraction rule.
The data extraction rules need to specify the number of data layers to be extracted. In general, the lower layer data is preferentially selected for transmission, for example, layer 1 may be selected, that is, the base layer data may be transmitted, or layers 1 to N may be selected, where the value of N may be set according to actual needs. According to the empirical value, the value of N is not more than 4.
Once the data extraction rule is determined, the required one or more layers of data are extracted for each macro block in the current image frame according to the same data extraction rule, thereby completing the data extraction of the entire current image frame.
S305, the extracted data is encoded, and the encoded data is stored or transmitted to an image receiving device through a network.
In this step, the extracted data may be encoded according to a preset encoding manner, where the preset encoding manner may be any one or more existing encoding manners.
Next, with reference to the embodiment of fig. 4, how the image receiving apparatus performs image processing will be described. Fig. 4 is a flowchart III of an image processing method according to an embodiment of the present disclosure, where the method is applied to an image receiving device, and the image receiving device is an image decoding end. As shown in fig. 4, the image processing method includes the steps of:
S401, receiving target data of each macro block transmitted by the image transmission apparatus.
Illustratively, the target data includes a base layer data and at least one enhancement layer data. Taking an 8×8 macroblock as an example, the target data includes first base layer data and second, third, and fourth enhancement layer data.
In this embodiment, each macroblock sent by the image sending apparatus is an encoded macroblock, and after receiving the target data of each macroblock sent by the image sending apparatus, the image receiving apparatus needs to decode each macroblock to generate a decoded macroblock corresponding to each macroblock.
S402, reducing the dimension of each macro block according to the gray level of the basic layer data of each macro block to obtain a frame image after the dimension reduction;
s403, enlarging the frame image after the dimension reduction into a frame image with a target dimension.
Since the block effect in the image is most obvious as the base layer data in the macro block, in the base layer data, since each divided 8×8 macro block only has a low frequency component, that is, the light and shadow intensity information of the image, the same gray scale value is in each 8×8 macro block, therefore, each 8×8 macro block can be subjected to dimension reduction according to the gray scale of the base layer data of the 8×8 macro block, for example, a 1920×1080 frame image is divided into 8×8 macro blocks, and then a 1920×1080 frame image is subjected to dimension reduction according to the gray scale of the base layer data of each macro block, so as to obtain a dimension reduced frame image, the dimension reduced frame image can be expressed as a 240×135 matrix, and the dimension reduced frame image is a low resolution image.
And then the frame image after the dimension reduction is amplified into a frame image with a target dimension by interpolation amplification, for example bicubic interpolation, for example, the frame image after the dimension reduction of the 240×135 matrix is amplified into a 1920×1080 frame image by interpolation. Although enlarged by interpolation, the frame image of the target size is still referred to as a low resolution image.
S404, performing super-resolution reconstruction on the frame image with the target size and the at least one enhancement layer data to generate a target frame image.
For example, the frame image of the target size and the at least one enhancement layer data may be super-resolution reconstructed using an interpolation-based super-resolution reconstruction algorithm, a reconstruction-based super-resolution reconstruction algorithm, a learning-based super-resolution reconstruction algorithm, or other super-resolution reconstruction algorithm to generate a target frame image.
In this embodiment, a super-resolution reconstruction algorithm based on learning is used to reconstruct the frame image of the target size and the at least one enhancement layer data. For example, the learning-based superresolution reconstruction algorithm may employ an image-based superresolution reconstruction convolutional neural network (Super-Resolution Convolutional Neural Network, SRCNN) model.
In this embodiment, the frame image of the target size and the at least one enhancement layer data are input into a pre-trained SRCNN neural network model, so as to generate a target frame image, where the target frame image is a high-resolution frame image.
Because the blocking effect of the 2 nd-4 th layer enhancement layer data in the image is not obvious, the process of dimension reduction and interpolation amplification is not needed to be executed, and the SRCNN model is directly input, so that the high-resolution image with the super-division deblocking effect can be reconstructed.
For enhancement layer data larger than layer 4, the superposition of the base layer data and the enhancement layer data makes the blocking effect of the enhancement layer data larger than layer 4 in the image almost not exist, and the visual perception of the user is not affected even if the enhancement layer data larger than layer 4 is not processed, so in the application, the image transmitting device does not transmit the enhancement layer data larger than layer 4 to the image receiving device, namely the target data of each macro block received by the image receiving device does not comprise the enhancement layer data larger than layer 4.
The image processing method provided by the embodiment of the disclosure can receive the target data of each macro block sent by the image sending equipment; reducing the dimension of each macro block according to the gray level of the basic layer data of each macro block to obtain a frame image after dimension reduction; amplifying the frame image after the dimension reduction into a frame image with a target dimension; the frame image with the target size and the at least one enhancement layer data are subjected to super-resolution reconstruction to generate a target frame image, only target data corresponding to each preprocessing macro block are received in the image transmission process, the transmitted data size is small, the problem that the data size in the image transmission process cannot be reduced to the greatest extent in the current image compression is solved, and the image processing method provided by the embodiment reduces the blocking effect of the target frame image and improves the resolution of the target frame image.
The image processing method provided in the embodiment of fig. 4 is described in further detail below in conjunction with the embodiment of fig. 5. Fig. 5 is a flowchart of an image processing method according to an embodiment of the present disclosure, where the method is applied to an image receiving device, and the image receiving device is an image decoding end. As shown in fig. 5, the image processing method includes the steps of:
S501, receiving frame image data sent by an image coding end;
S502, decoding the received frame image data to obtain one or more layers of image data;
In this step, the currently received code stream is decoded according to a decoding scheme corresponding to the encoding scheme employed by the image encoding terminal. The one or more layers of image data are one base layer data per macroblock or one base layer data and at least one enhancement layer data per macroblock.
And S503, performing super-resolution reconstruction on one or more layers of image data obtained in the step S502 by adopting SRCNN model, and obtaining a high-definition image after reconstruction.
How to reconstruct an image according to the SRCNN model is explained below.
By analyzing the layered image, it is found that the block effect is most obvious in the first layer image, i.e. the base layer data, in the first layer image data, since each divided 8×8 macroblock has only a low frequency component, i.e. the light intensity information of the image, and therefore each 8×8 macroblock has the same gray value, for the first layer image, each 8×8 macroblock can be represented by the gray value of the base layer data, for example, 1920×1080 images are represented as 240×135 matrices, and then the image is restored to the original image size by the super resolution algorithm.
There are many existing superdivision methods, such as interpolation-based superdivision reconstruction, reconstruction-based superdivision reconstruction, and learning-based superdivision reconstruction. The learning-based method is to learn a certain corresponding relation between the low-resolution image and the high-resolution image by using a large amount of training data, and then predict the high-resolution image corresponding to the low-resolution image according to the learned mapping relation, so as to realize the super-resolution reconstruction process of the image.
The scheme adopts SRCNN model to reconstruct super resolution of low resolution image. The SRCNN model is based on deep learning method to reconstruct super-division of image, and end-to-end mapping between low resolution and high resolution images is realized by adopting SRCNN model, and the structure of SRCNN model is shown in fig. 6.
For a low-resolution image, firstly, bicubic interpolation is adopted to amplify the low-resolution image into an image target, then, nonlinear mapping is fitted through a three-layer convolution network, and finally, a high-resolution image result is output. The image reconstruction process adopting SRCNN model is as follows:
Step 1: and (5) extracting image blocks. The low resolution image is first scaled up to the target size using bicubic interpolation, e.g., 240 x 135 to 1920 x 1080 using interpolation. Although enlarged by interpolation, it is still called a low resolution image, i.e., a super-resolution input image. Image blocks are extracted from the low-resolution input image to form a high-dimensional feature map.
F1(Y)＝max(0,W1*Y+B1)
Wherein, W 1 and B 1 are super-division parameters, which are obtained by learning, F 1 (Y) is the characteristic value of the high-dimensional characteristic diagram, and Y is the characteristic value of the amplified low-resolution image. Illustratively, the high-dimensional feature map can be obtained by inputting the low-resolution image after the method into the first-layer convolution of the SRCNN model. The convolution kernel size 9*9 (f 1×f1) of the first layer convolution, the number of convolution kernels 64 (n 1), and the first layer convolution calculation can output 64 high-dimensional feature graphs.
Step 2: nonlinear mapping. Inputting the high-dimensional characteristic number graph output by the first layer convolution into a second layer convolution, wherein the convolution kernel size of the second layer convolution is 1*1 (f 2 x f 2), the number of the convolution kernels is 32 (n 2), and the second layer convolution can calculate and output 32 characteristic graphs; this process enables nonlinear mapping of two high-dimensional feature vectors.
F2(Y)＝max(0,W2*F1(Y)+B2)
Wherein W 2 = n1 x n2. By 1*1 convolutions, B 2 is the super-division parameter, and F 2 (Y) is the characteristic value of the 32 characteristic diagrams.
Step 3: reconstruction. And inputting 32 feature images output by the second layer convolution into a third layer convolution, wherein the convolution kernel of the third layer convolution is 5*5 (f 3 x f 3), the number of the convolution kernels is 1 (n 3), and outputting 1 feature image, namely, the final reconstructed high-resolution image.
F3(Y)＝W3*F2(Y)+B3
Wherein, W 3 and B 3 are super-division parameters, and F 3 (Y) is the characteristic value of the final reconstructed high-resolution image.
Illustratively, the parametric structure of the SRCNN model is as in fig. 7, and the parametric structure of the SRCNN model includes:
first layer convolution: convolution kernel size 9×9 (f1×f1), number of convolution kernels 64 (n 1), and output 64 feature maps;
second layer convolution: convolution kernel size 1×1 (f2×f2), convolution kernel number 32 (n 2), and output 32 feature maps;
third layer convolution: the convolution kernel size is 5 multiplied by 5 (f3 multiplied by f 3), the convolution kernel number is 1 (n 3), and 1 feature map is output, namely the final reconstructed high-resolution image.
For the layer 2 to layer 4 frame images, the block effect is not obvious, so that the process of downsampling and re-interpolation, namely the step1 process, is not needed, the images are directly subjected to step2 nonlinear mapping, and then the step3 is reconstructed to obtain the super-division deblocking effect image.
For frame images larger than layer 4, the superposition of the base layer and the enhancement layer makes the image blocking effect less obvious, and does not affect the visual perception even if not processed, so the scheme does not process the frame images transmitted to the layer 4.
How to train SRCNN the model is described below in connection with the embodiment of fig. 8. Fig. 8 is a flowchart of a SRCNN model training method provided by an embodiment of the present disclosure. As shown in fig. 8, the method includes:
s801, generating a training sample set;
Firstly, collecting images;
to achieve training of the neural network, a large number of images need to be acquired to generate a training sample set.
Secondly, layering the acquired images;
Thirdly, extracting image data of a corresponding layer according to a preset data extraction rule;
fourthly, firstly encoding the extracted image data and then decoding;
fifthly, taking the decoded image data and original pictures as a training sample set;
Wherein the training set comprises a plurality of training samples, each of which may be represented as (Ai, bi), wherein Ai represents the decoded image data, i.e. the input data; bi represents an original, i.e., a target image.
S802, training the SRCNN model through a training sample set.
Specifically, the decoded image data is used as the input of a SRCNN model, and the output of a SRCNN model is calculated; calculating the difference between the output of the SRCNN model and the actual value (target), adjusting SRCNN parameters (weights) of the model according to the obtained difference, and continuously iterating the steps until the difference between the output of the SRCNN model and the actual value is smaller than a preset error threshold.
The SRCNN model finally obtained is used as a pre-trained SRCNN model used in the invention and is used for super-division reconstruction of the low-resolution image at the image decoding end.
Fig. 9 is a block diagram of an image processing apparatus according to an embodiment of the present disclosure, which is applied to an image transmission device. As shown in fig. 9, the apparatus 90 includes:
The frame image to be processed acquisition module 901 is configured to acquire a frame image to be processed, and perform macro block division on the frame image to be processed to generate at least one macro block;
A macroblock preprocessing module 902, configured to preprocess each macroblock in the at least one macroblock to obtain a preprocessed macroblock corresponding to the each macroblock, where each preprocessed macroblock includes a DC component and at least one AC component;
the preprocessing macroblock layering module 903 is configured to layer each preprocessing macroblock according to a preset layering rule, so as to obtain layering data corresponding to each preprocessing macroblock, where the layering data includes a base layer and at least one enhancement layer, the DC component is the base layer, the AC component is divided into at least one enhancement layer according to an importance degree, and the importance degrees of the AC components in the same enhancement layer are the same;
The layered data extraction module 904 is configured to extract layered data corresponding to each of the preprocessed macro blocks according to a preset data extraction rule, so as to obtain target data corresponding to each of the preprocessed macro blocks, where the target data includes a base layer data and at least one enhancement layer data;
the target data sending module 905 is configured to send the target data corresponding to each of the preprocessed macro blocks to the image receiving apparatus.
In one embodiment, the target data includes one base layer data and three enhancement layer data, the three enhancement layer data being the most important three enhancement layer data in the AC component.
In one embodiment, the macroblock preprocessing module 902 is specifically configured to:
Performing DCT on each macro block in the at least one macro block to obtain a transformed macro block corresponding to each macro block, wherein the transformed macro block comprises a DC component and at least one AC component;
and carrying out quantization processing on each transformed macroblock to obtain a quantized macroblock corresponding to each transformed macroblock, wherein the quantized macroblock comprises a quantized DC component and at least one quantized AC component.
In one embodiment, as shown in FIG. 10, the apparatus 90 further comprises:
And the macroblock encoding module 906 is configured to encode the target data corresponding to each of the preprocessed macroblocks to obtain encoded macroblocks corresponding to each of the preprocessed macroblocks.
The implementation process and technical effects of the image processing apparatus provided in the embodiments of the present disclosure may be referred to the embodiments of fig. 1 to 3, and are not described herein.
Fig. 11 is a block diagram III of an image processing apparatus according to an embodiment of the present disclosure, which is applied to an image receiving device. As shown in fig. 11, the apparatus 110 includes:
a target data receiving module 1101, configured to receive target data of each macro block according to the first aspect sent by the image sending device;
the macro block dimension reduction module 1102 is configured to reduce the dimension of each macro block according to the gray level of the base layer data of each macro block, so as to obtain a frame image after dimension reduction;
a frame image amplifying module 1103, configured to amplify the reduced frame image into a frame image with a target size;
A target frame image generating module 1104, configured to perform super-resolution reconstruction on the frame image of the target size and the at least one enhancement layer data, and generate a target frame image.
In one embodiment, as shown in fig. 12, the apparatus 110 further comprises:
the macroblock decoding module 1105 is configured to decode each macroblock, and generate a decoded macroblock corresponding to each macroblock.
The implementation process and technical effects of the image processing apparatus provided in the embodiments of the present disclosure may be referred to the embodiments of fig. 4 to 7, and are not described herein.
Fig. 13 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present disclosure, and as shown in fig. 14, the image processing apparatus 130 includes:
A processor 1301 and a memory 1302, the memory 1301 having stored therein at least one computer instruction, which is loaded and executed by the processor 1301 to implement the steps performed in the image processing method described in the corresponding embodiment of fig. 1 to 3.
Based on the image processing method described in the above-described embodiments corresponding to fig. 1 to 3, the embodiments of the present disclosure also provide a computer-readable storage medium, for example, a non-transitory computer-readable storage medium may be a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, or the like. The storage medium stores computer instructions for executing the image processing method described in the embodiments corresponding to fig. 1 to 3, which are not described herein.
Fig. 14 is a schematic diagram ii of a structure of an image processing apparatus according to an embodiment of the present disclosure, and as shown in fig. 14, the image processing apparatus 140 includes:
A processor 1401 and a memory 1402, said memory 1401 having stored therein at least one computer instruction, said instructions being loaded and executed by said processor 1401 to implement the steps performed in the image processing method described in the corresponding embodiments of fig. 4 to 7.
Based on the image processing method described in the above-described embodiments corresponding to fig. 4 to 7, the embodiments of the present disclosure also provide a computer-readable storage medium, for example, a non-transitory computer-readable storage medium may be a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, or the like. The storage medium stores computer instructions for executing the image processing method described in the embodiments corresponding to fig. 4 to 7, which are not described herein.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. An image processing method, characterized by being applied to an image transmission apparatus, comprising:
obtaining a frame image to be processed, and carrying out macro block division on the frame image to be processed to generate at least one macro block;
Preprocessing each macro block in the at least one macro block to obtain a preprocessed macro block corresponding to each macro block, wherein each preprocessed macro block comprises a Direct Current (DC) component and at least one Alternating Current (AC) component;
Layering each pre-processed macro block according to a preset layering rule to obtain layering data corresponding to each pre-processed macro block, wherein the layering data comprise a base layer and at least one enhancement layer, the DC component is the base layer, the AC component is divided into at least one enhancement layer according to the importance degree, and the importance degree of the AC component in the same enhancement layer is the same;
Extracting layered data corresponding to each pre-processed macro block according to the same preset data extraction rule to obtain target data corresponding to each pre-processed macro block, wherein the target data comprises a basic layer data and at least one enhancement layer data, the preset data extraction rule prescribes the number of extracted data layers, and the number of data layers is less than or equal to 4;
And transmitting the target data corresponding to each preprocessing macro block to an image receiving device.
2. The method of claim 1, wherein the target data comprises one base layer data and three enhancement layer data, the three enhancement layer data being the most significant three enhancement layer data in the AC component.
3. The method of claim 1, wherein the pre-processing each of the at least one macroblock comprises:
performing Discrete Cosine Transform (DCT) on each macro block in the at least one macro block to obtain a transformed macro block corresponding to each macro block, wherein the transformed macro block comprises a DC component and at least one AC component;
and carrying out quantization processing on each transformed macroblock to obtain a quantized macroblock corresponding to each transformed macroblock, wherein the quantized macroblock comprises a quantized DC component and at least one quantized AC component.
4. The method according to claim 1, wherein before the target data corresponding to each of the preprocessed macro blocks is transmitted to the image receiving device, the method further comprises:
And encoding the target data corresponding to each preprocessing macro block to obtain an encoded macro block corresponding to each preprocessing macro block.
5. An image processing method, characterized by being applied to an image receiving apparatus, comprising:
receiving the target data of each macro block according to any one of claims 1 to 2 transmitted by the image transmission apparatus;
Performing dimension reduction on each macro block according to the gray level of the basic layer data of each macro block to obtain a dimension reduced frame image;
Amplifying the frame image after the dimension reduction into a frame image with a target dimension;
and carrying out super-resolution reconstruction on the frame image with the target size and the at least one enhancement layer data to generate a target frame image.
6. The method of claim 5, wherein super-resolution reconstructing the frame image of the target size and the at least one enhancement layer data comprises:
the frame image of the target size and the at least one enhancement layer data are input into a pre-trained super-resolution reconstruction convolutional neural network SRCNN model.
7. The method according to claim 5, wherein each macro block transmitted by the image transmission apparatus is an encoded macro block, and wherein after receiving the target data of each macro block transmitted by the image transmission apparatus, the method further comprises:
and decoding each macro block to generate a decoded macro block corresponding to each macro block.
8. An image processing apparatus, characterized by being applied to an image transmission device, comprising:
The frame image processing device comprises a frame image acquisition module to be processed, a frame image processing module and a frame image processing module, wherein the frame image acquisition module to be processed is used for acquiring a frame image to be processed, and carrying out macro block division on the frame image to be processed to generate at least one macro block;
a macro block preprocessing module, configured to preprocess each macro block in the at least one macro block to obtain a preprocessed macro block corresponding to each macro block, where each preprocessed macro block includes a DC component and at least one AC component;
the preprocessing macro block layering module is used for layering each preprocessing macro block according to a preset layering rule to obtain layering data corresponding to each preprocessing macro block, wherein the layering data comprise a basic layer and at least one enhancement layer, the DC component is the basic layer, the AC component is divided into at least one enhancement layer according to the importance degree, and the importance degree of the AC components in the same enhancement layer is the same;
The hierarchical data extraction module is used for extracting the hierarchical data corresponding to each preprocessing macro block according to the same preset data extraction rule to obtain target data corresponding to each preprocessing macro block, wherein the target data comprises a basic layer data and at least one enhancement layer data, the preset data extraction rule prescribes the number of extracted data layers, and the number of data layers is less than or equal to 4;
and the target data sending module is used for sending the target data corresponding to each preprocessing macro block to the image receiving equipment.
9. The apparatus of claim 8, wherein the macroblock preprocessing module is specifically configured to:
Performing DCT on each macro block in the at least one macro block to obtain a transformed macro block corresponding to each macro block, wherein the transformed macro block comprises a DC component and at least one AC component;
and carrying out quantization processing on each transformed macroblock to obtain a quantized macroblock corresponding to each transformed macroblock, wherein the quantized macroblock comprises a quantized DC component and at least one quantized AC component.
10. An image processing apparatus, characterized by being applied to an image receiving device, comprising:
a target data receiving module for receiving target data of each macro block according to any one of claims 1 to 2 transmitted by the image transmitting apparatus;
The macro block dimension reduction module is used for reducing the dimension of each macro block according to the gray level of the basic layer data of each macro block to obtain a frame image after dimension reduction;
the frame image amplifying module is used for amplifying the frame image subjected to the dimension reduction into a frame image with a target dimension;
and the target frame image generation module is used for carrying out super-resolution reconstruction on the frame image with the target size and the at least one enhancement layer data to generate a target frame image.
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Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
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| KR20120038355A (en) * | 2010-10-13 | 2012-04-23 | 삼성전자주식회사 | Method and apparatus of entropy encoding/decoding and symbol endcoding/decoding method and apparatus therefor |
| TWI627857B (en) * | 2012-06-29 | 2018-06-21 | Sony Corp | Image processing device and method |
| WO2014009603A1 (en) * | 2012-07-02 | 2014-01-16 | Nokia Corporation | Method and apparatus for video coding |
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-
2020
- 2020-06-23 CN CN202010581960.7A patent/CN111800633B/en active Active
Non-Patent Citations (2)
| Title |
|---|
| 基于小波变换的空间可分级图像压缩编码算法;李永忠;计算机应用与软件;20050212(02);全文 * |
| 基于感兴趣区域率失真优化的视频压缩编码通信系统设计;张哲为;《博士电子期刊》;20200115;全文 * |
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