WO2018068532A1 - 图像编解码装置、图像处理系统、图像编解码方法和训练方法 - Google Patents
图像编解码装置、图像处理系统、图像编解码方法和训练方法 Download PDFInfo
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Definitions
- the present disclosure relates to an image encoding device, an image decoding device, an image processing system including the image encoding and decoding device, a training method for the image processing system, and a display device.
- an embodiment of the present disclosure provides an image encoding apparatus including: a first image input configured to provide a first image; and a plurality of second image inputs configured to provide a plurality of a second image; a first convolutional neural network module coupled to the plurality of second image inputs, configured to update features of each of the plurality of second images to obtain a corresponding An update feature; an image overlay module coupled to the first image input end, the first convolutional neural network module, configured to update an update feature of each of the plurality of second images The first image is superimposed to generate a superimposed image, and the superimposed image is output; a prediction module is coupled to the image superimposing module, configured to generate a plurality of predicted images according to the superimposed image; and an image difference acquiring module, and The plurality of second image inputs, the prediction module are connected, configured to determine a difference feature of each of the plurality of second images and the corresponding predicted image, Wherein different output; an output interface configured to output the difference image and the overlay
- the prediction module is a second convolutional neural network module.
- an image encoding apparatus comprising: a first image input end configured to acquire a first image; and a plurality of second image input ends configured to acquire a plurality of second images; a module, coupled to the plurality of second image inputs, configured to update features of each of the plurality of second images to obtain corresponding updated features; an image overlay module, and the first An image input end, the feature module is connected, configured to superimpose an updated feature of each of the plurality of second images with the first image to generate a superimposed image, and output the superimposed image; a convolutional neural network module, coupled to the image overlay module, configured to generate a plurality of predicted images according to each of the superimposed images; and an image difference acquisition module, and the plurality of second image input ends, a prediction module connection configured to determine a difference feature of each of the plurality of second images from a corresponding predicted image, output the difference feature; an output interface configured Wherein said difference image and output the superimposed.
- the image encoding apparatus further includes: a splitting unit connected to the first image input end and the plurality of second image input ends, configured to split the input original image to obtain The first image and the plurality of second images.
- a splitting unit connected to the first image input end and the plurality of second image input ends, configured to split the input original image to obtain The first image and the plurality of second images.
- the image superimposing module superimposes an updated feature of each of the plurality of second images with the first image in accordance with an additive right.
- an image encoding device configured to multiply the first image by a first weight parameter to obtain a first product, and multiply the updated feature by a second weight parameter Obtaining a second product, superimposing the first product and the second product to generate a superimposed image; wherein the first weight parameter is greater than 0, and a sum of the first weight parameter and the second weight parameter is 1.
- An image encoding apparatus further comprising: a splitting unit connected to the first image input end and the plurality of second image input ends, configured to split the input original image Obtaining the first image and the plurality of second images.
- the image encoding device configured to split the original image into 2n images, the number of the first images is 1, and the number of the second images is 2n -1, n is an integer greater than zero.
- an embodiment of the present disclosure provides an image decoding apparatus including: a superimposed image input configured to receive a superimposed image; a difference feature input configured And a prediction module, connected to the superimposed image input end, configured to generate a plurality of predicted images according to the superimposed image; and a difference difference module, connected to the difference feature input end and the prediction module, And configured to generate a plurality of second images according to the plurality of predicted images and the difference features, and output the plurality of second images; and connect the fourth convolutional neural network module to the de-differentiation module, And configured to update each of the plurality of second images to obtain a corresponding update feature; and an image de-overlay module coupled to the overlay image input terminal and the fourth convolutional neural network module, And configured to perform de-overlaying on the superimposed image according to the update feature to obtain a first image, and output the first image; and outputting, configured to output the plurality of second images and the first An image.
- the prediction module is a third convolutional neural network module.
- an image decoding apparatus comprising: a superimposed image input configured to receive a superimposed image; a difference feature input configured to receive a difference feature; a third convolutional neural network module And connecting to the superimposed image input end, configured to generate a plurality of predicted images according to the superimposed image; and the difference difference module is connected to the difference feature input end and the third convolutional neural network module, And configured to generate a plurality of second images according to each of the plurality of predicted images and the difference feature, and output the plurality of second images; and the feature module is connected to the demodastic module, Configuring to update each of the plurality of second images to obtain a corresponding update feature; and an image de-overlay module coupled to the overlay image input, the feature module, configured to Updating features performing de-overlaying on the superimposed image to obtain a first image, outputting the first image; and outputting, configured to output the plurality of second images And the first image.
- a splicing unit connected to the output end, configured to splicing the first image and the plurality of second images to obtain a decoded image, and outputting The interface outputs the decoded image.
- the image de-overlaying module is configured to perform de-overlaying on the superimposed image according to the update feature and its superimposition weight.
- An image decoding device configured to multiply the update feature by a second weight parameter to obtain a second product, and remove the second product from the superimposed image Obtaining a first product, dividing the first product by a first weight parameter to obtain the first image; wherein the first weight parameter is greater than 0, and the first weight parameter is The sum of the second weight parameters is 1.
- an embodiment of the present disclosure provides an image processing system including: image encoding apparatus, including: a first image input configured to acquire a first image; and a plurality of second image inputs Configuring to acquire a plurality of second images; a first convolutional neural network module coupled to the plurality of second image inputs, configured to update features of each of the plurality of second images And obtaining an updated feature; the image overlay module is coupled to the first image input end, the first convolutional neural network module, and configured to update each of the plurality of second images A feature is superimposed with the first image to generate a superimposed image, and the superimposed image is output; a first prediction module, coupled to the image superimposition module, configured to generate a plurality of predicted images according to each of the superimposed images And an image difference obtaining module, coupled to the plurality of second image inputs, the prediction module, configured to determine each of the plurality of second images and the corresponding a difference feature of the image, the difference feature is output; the output interface is configured
- the first prediction module is a second convolutional neural network module
- the second prediction module is a third convolutional neural network module.
- an image processing system includes: an image encoding device having a first image input configured to acquire a first image; and a plurality of second image inputs configured to acquire a plurality of a second image; a first feature module coupled to the plurality of second image inputs, configured to update features of each of the plurality of second images to obtain corresponding updated features; image overlay a module, coupled to the first image input end, the first feature module, configured to superimpose an updated feature of each of the plurality of second images with the first image Generating a superimposed image, outputting the superimposed image; a second convolutional neural network module coupled to the image superimposition module, configured to generate a plurality of predicted images according to each of the superimposed images; and an image difference acquisition module And connecting to the plurality of second image input ends and the second convolutional neural network module, configured to determine a difference feature of each of the plurality of second images and the corresponding predicted image, a difference feature output; an output interface configured to output the superimposed image and the difference
- the first feature module is a first convolutional neural network module
- the second feature module is a fourth convolutional neural network module.
- the quantization device being coupled to the image encoding device, configured to receive the superimposed image and the difference feature outputted from the output interface, Performing a quantization process and an inverse quantization process on the superimposed image and the difference feature to generate a quantized superimposed image and a quantized difference feature; and the image decoding device configured to a superimposed image input end of the image decoding device,
- the difference feature input terminal outputs the quantized overlay image and the quantized difference feature.
- quantization device configured to perform the quantization process on the superimposed image and the difference feature using a uniform step quantization function USQ,
- the quantization means is configured to perform the inverse quantization process on an output q of the uniform step quantization function USQ using an inverse uniform step quantization function InvUSQ to generate the quantization Superimposing the image and the quantized difference feature,
- an image encoding method comprising the steps of:
- update and/or the prediction employs a convolutional neural network.
- the image encoding method further includes the following steps:
- the input original image is split into the first image and the plurality of the second images.
- an image decoding method comprising the steps of: receiving a superimposed image and a difference feature; generating a plurality of predicted images according to the superimposed image; according to each of the plurality of predicted images And generating, by the difference feature, a plurality of second images; updating each of the plurality of second images to obtain a corresponding update feature; performing de-overlapping on the superimposed image according to the update feature to obtain a first image Outputting the plurality of second images and the first image; wherein the updating and/or the predicting uses a convolutional neural network.
- the image decoding method further includes the step of splicing the first image and the plurality of second images to obtain a decoded image.
- an embodiment of the present disclosure provides a training method for an image processing system, including: selecting a fixed quantization parameter; inputting a training image to the image processing system, and adjusting a convolutional nerve a weight of each filtering unit in each convolutional layer in the network module, running a finite number of iterations to optimize the objective function; and reducing the quantization parameter by a predetermined value, if the quantization parameter is not less than a predetermined threshold, repeating The training step of the objective function optimization, otherwise it ends The training method.
- X represents the input training image
- OUT represents an output image
- MSE is a mean square error function between the input training image and the output image.
- embodiments of the present disclosure provide a display device including the aforementioned image encoding device, image decoding device, and/or image processing system.
- 1 is a schematic diagram illustrating a convolutional neural network for image processing
- FIG. 2 is a schematic diagram illustrating a wavelet transform for multi-resolution image transformation
- FIG. 3 is a schematic structural diagram of an image processing system for implementing wavelet transform using a convolutional neural network
- FIG. 4 is a block diagram showing the structure of an image encoding device according to a first embodiment of the present disclosure
- FIG. 5 is a block diagram illustrating a structure of an image decoding apparatus according to a first embodiment of the present disclosure
- FIG. 6 is a block diagram illustrating a structure of an image processing system according to a second embodiment of the present disclosure
- FIG. 7 is a flowchart illustrating a training method according to a third embodiment of the present disclosure.
- FIG. 1 illustrates a schematic diagram of a convolutional neural network for image processing.
- a convolutional neural network for image processing uses an image as an input and an output, and replaces the scalar weight by a filter (i.e., convolution).
- a convolutional neural network with a simple structure of 3 layers is shown in FIG.
- FIG. 1 four input images are input to the input layer 101, three cells are present in the middle hidden layer 102 to output three output images, and two cells are output in the output layer 103 to output two output images.
- Input layer 101 has weight
- Each box corresponds to a filter, where k is a label indicating the input layer number, and i and j are labels indicating the input and output units, respectively.
- Bias Is the scalar added to the output of the convolution.
- the result of the addition of some convolutions and offsets then passes through an activation box, which typically corresponds to a rectifying linear unit (ReLU), a sigmoid function, or a hyperbolic tangent function.
- ReLU rectifying linear unit
- sigmoid function a sigmoid function
- hyperbolic tangent function a hyperbolic tangent function.
- each filter and bias is fixed during operation of the system.
- Each filter and offset is obtained in advance by using a set of input/output sample images and adjusting to meet some application-dependent optimization criteria.
- FIG. 2 illustrates a schematic diagram of a wavelet transform for multi-resolution image transformation.
- Wavelet transform is a multi-resolution image transform for image codec processing, and its applications include transform coding in the JPEG 2000 standard.
- image encoding (compression) processing wavelet transform is used to represent an original high resolution image with a smaller low resolution image (eg, a portion of the original image).
- image decoding (decompression) process the inverse wavelet transform is used to recover the original image using the low resolution image and the difference features required to restore the original image.
- Fig. 2 schematically shows a 3-level wavelet transform and an inverse transform.
- one of the smaller low resolution images is the reduced version A of the original image, while the other low resolution images represent the missing details (Dh, Dv, and Dd) needed to restore the original image.
- FIG. 3 is a schematic diagram showing the structure of an image processing system that implements wavelet transform using a convolutional neural network.
- the lifting scheme is an effective implementation of wavelet transform and a flexible tool for constructing wavelets.
- Fig. 3 schematically shows a standard structure for 1D data.
- the left side of Fig. 3 is the encoder 31.
- the splitting unit 302 in the encoder 31 converts the input original image 301 into a low resolution image A and a detail D.
- the encoder 31 further uses the prediction filter p and the update filter u. For compression applications, it is desirable that D be about 0 so that most of the information is contained in A.
- the right side of FIG. 3 is the decoder 32.
- the parameters of the decoder 32 are exactly the same as the filters p and u from the encoder 31, but only the filters p and u are arranged oppositely. Due to the strict correspondence of the encoder 31 and the decoder 32, this configuration ensures that the decoded image 304 obtained by splicing via the splicing unit 303 of the decoder 32 is identical to the original image 301. Further, the structure shown in FIG. 3 is not limited, and may alternatively be configured in the order of the update filter u and the prediction filter p in the decoder.
- FIG. 4 is a schematic structural diagram illustrating an image encoding device according to a first embodiment of the present disclosure.
- the image encoding device 40 includes:
- the splitting unit 402 is configured to split the input original image to obtain a first image UL and a plurality of second images UR, BR, BL.
- a first image input 403 is configured to receive the first image UL from the split unit 402.
- a plurality of second image inputs 404, 405, 406 are configured to receive a plurality of second images UR, BR, BL from the splitting unit 402, respectively.
- a first convolutional neural network module 407 coupled to the plurality of second image inputs 404, 405, 406, configured to update UR, BR, BL for the plurality of second images to obtain corresponding update features .
- the first convolutional neural network module 407 may be an update filter described with reference to FIG.
- An image superimposition module 408 is coupled to the first image input terminal 403, the first convolutional neural network module 407, and the output interface 411, and configured to superimpose the updated feature U and the first image according to UL
- the superimposed image A is generated by overlapping, and the superimposed image A is output through the output interface 411.
- the image superimposition module 408 is configured to multiply the first image UL by a first weight parameter a to obtain a first product, and multiply the updated feature U by a second weight parameter. b obtains a second product, superimposes the first product and the second product to generate a superimposed image A.
- the first weight parameter a is greater than 0, and the sum of the first weight parameter a and the second weight parameter b is 1. That is, in the image overlay module 408:
- the second convolutional neural network module 409 is coupled to the image overlay module 408 and configured to generate a plurality of predicted images based on the superimposed image A.
- An image difference obtaining module 410 is connected to the plurality of second image input ends 404, 405, 406, the second convolutional neural network module 409, and the output interface 411, and configured to determine the plurality of second each of the predicted image corresponding to a difference feature D h, D d D v and images UR, BR, BL, and wherein the difference D h, D d and D v through the output interface 411 outputs.
- the first convolutional neural network module 407, the image superimposition module 408, the second convolutional neural network module 409, and the image difference acquisition module 410 constitute image coding.
- a compression unit of the device 40 performs image compression based on the wavelet transform of the lifting scheme on the first image UL and the plurality of second images UR, BR, BL input from the splitting unit 402.
- FIG. 5 is a block diagram illustrating a structure of an image decoding apparatus according to a first embodiment of the present disclosure.
- the image decoding device shown in Fig. 5 can be used to decode the output image of the image encoding device shown in Fig. 4.
- the image decoding device 50 includes:
- the superimposed image input terminal 507 is configured to receive the superimposed image A.
- the difference feature inputs 504, 505, and 506 are configured to receive the difference features D h , D d , and D v , respectively .
- the superimposed image A may be image data from the image superimposing module 408 output from the output interface 411 of the image encoding device 40 shown in FIG.
- the difference features D h , D d , and D v may be image data from the image difference acquisition module 410 output from the output interface 411 of the image encoding device 40 illustrated in FIG. 4 .
- the third convolutional neural network module 507 is coupled to the superimposed image input terminal 507 and configured to generate a plurality of predicted images based on the superimposed image A.
- a difference difference module 508 coupled to the difference feature inputs 504, 505, and 506, the third convolutional neural network module 507, and the outputs 512, 513, and 514, configured to be based on the plurality of predicted images and
- the difference features D h , D d and D v generate a plurality of second images UR, BR, BL, and output the plurality of second images through the outputs 512, 513 and 514.
- a fourth convolutional neural network module 509 coupled to the disparity module 508, is configured to update the plurality of second images to obtain a corresponding update feature U.
- An image de-overlay module 510 is coupled to the superimposed image input terminal 503, the fourth convolutional neural network module 509, and the output terminal 511, and configured to add the overlay according to the updated feature U and its superimposed weights
- the image A is subjected to de-overlapping to obtain a first image UL, and the first image UL is output through the output terminal 511.
- the image de-overlay module 510 is configured to multiply the update feature by a second weight parameter b to obtain a second product bU, and remove the second from the superimposed image A. Multiplying a product to obtain a first product (A-bU), dividing the first product (A-bU) by a first weight parameter a to obtain the first image UL; wherein the first weight parameter is greater than 0, and Place The sum of the first weight parameter and the second weight parameter is 1. That is, in the image overlay module 510:
- the image de-overlay module 510 performs the inverse processing with the image overlay module 408, wherein the first weight parameter and the second weight parameter satisfy the same condition.
- the first image UL output by the image de-overlay module 510 may be the same as the first image obtained by splitting the original image.
- the splicing unit 502 is connected to each of the output terminals 511-514 and the output interface 515, and is configured to splicing the first image UL and the plurality of second images UR, BR, BL to obtain a decoded image 501, and The output interface 515 outputs the decoded image 501.
- the filtering parameters of the network module 409 and the first convolutional neural network module 407 are the same, and the de-overlapping process performed by the image de-emphasis module 510 in the image decoding device 50 shown in FIG. 5 and the image encoding device shown in FIG. 4 are performed.
- the superimposing process performed by the image superimposing module 408 in 40 is completely reversed, and the difference between the de-difference process performed by the disparity module 508 in the image decoding device 50 shown in FIG.
- the difference obtaining process performed by the obtaining module 410 is completely reversed, and the image encoding device 40 shown in FIG. 5 can be accurately decoded to restore the image encoded by the image encoding device 40, and the filtering is performed with each convolutional neural network. Parameters are irrelevant.
- the update process is completed by the first convolutional neural network module 407
- the prediction process is completed by the second convolutional neural network module 409, in the specific application, by the first convolutional neural network
- the module 407 and the second convolutional neural network module 409 perform corresponding training such that the first convolutional neural network module 407 and the second convolutional neural network module 409 have optimized filtering parameters, thereby making the image encoding apparatus have a higher
- the compression ratio without the need to manually set the corresponding filtering parameters, reduces the setting complexity of the filtering parameters.
- the weight parameter a for image superposition is set, thereby further improving the downscaling of the encoder and the performance and degree of freedom of the scale of the decoder.
- the output difference characteristics D h , D d , and D v are approximately 0, and the overall compression ratio can be approximately 75%.
- the images UR, BR, and BL are predicted with the first image UL on the upper left side.
- images UR, BR, and BL may also be utilized as the first image to predict other images.
- the number of first images is one.
- two images may be predicted using two images, or three images may be predicted.
- the theoretical maximum value of the compression ratio of the two images is predicted to be 50%, and three images are used.
- the theoretical maximum of the compression ratio of the predicted three images is 25%.
- a single-stage compression system composed of a first convolutional neural network module 407 and a second convolutional neural network module 409 is employed.
- the present disclosure is not limited thereto, and in an alternative embodiment, a two-stage and more-stage compression configuration may be employed.
- the image decoding device 50 shown in FIG. 5 accurately decodes and restores the image encoded by the image encoding device 40 shown in FIG. 4, that is, the image encoding device 40 shown in FIG. 4 and the image decoding device 50 shown in FIG. Form a lossless system.
- a standard such as JPEG2000
- FIG. 6 is a block diagram illustrating an image processing system according to a second embodiment of the present disclosure.
- the image processing system 6 according to the second embodiment of the present disclosure shown in FIG. 6 includes the image encoding device 40 shown in FIG. 4 and the image decoding device 50 shown in FIG. 5, and the image processing system according to the second embodiment of the present disclosure. 6 also includes a quantification device 60.
- the quantization device 60 is connected to the image encoding device 40 and the image decoding device 50.
- the structure and input and output of the image encoding device 40 in Fig. 6 are the same as those described with reference to Fig. 4, and a repetitive description thereof will be omitted herein.
- a quantization device 60 is coupled to the image encoding device 40 and configured to receive the superimposed image A and the difference features D h , D d and D v output from the output interface 411 for the superimposed image A and
- the difference features D h , D d , and D v perform quantization processing and inverse quantization processing to generate a quantized superimposed image and a quantized difference feature.
- the quantization device is configured to perform the quantization process on the superimposed image and the difference feature using a uniform step quantization function USQ,
- I the smallest integer less than x
- ⁇ is the quantization parameter
- the quantization means is configured to perform the inverse quantization process on the output q of the uniform step quantization function USQ using an inverse uniform step quantization function InvUSQ to generate the quantized superimposed image and the quantized difference feature, among them
- the corresponding filtering parameters can be set for each convolutional neural network module by performing corresponding training by the convolutional neural network module in the encoding device and the decoding device.
- the quantization parameter ⁇ is introduced, it is necessary to provide training for simultaneously training the convolutional neural network module and the quantization module in the encoding device and the decoding device. method.
- FIG. 7 illustrates a flow chart of a training method in accordance with a third embodiment of the present disclosure. As shown in FIG. 7, the training method according to the third embodiment of the present disclosure includes the following steps.
- step S701 the fixed quantization parameter ⁇ is selected.
- an initial value for example, 1000
- the larger quantization parameter ⁇ such that its output is similar to a lossless system, the optimization problem is easily solved. Thereafter, the processing proceeds to step S702.
- step S702 if the quantization parameter ⁇ is fixed, the training image is input to the image processing system, and the weights of the filtering units in each convolution layer in the first to fourth convolutional neural network modules are adjusted. Run a limited iteration to optimize the objective function.
- the objective function is:
- X represents the input training image
- OUT represents an output image
- MSE is a mean square error function between the input training image and the output image.
- step S703 the quantization parameter is decreased by a predetermined value.
- the quantization parameter can be reduced by 5%. Thereafter, the processing proceeds to step S704.
- step S704 it is determined whether the quantization parameter is not less than a predetermined threshold.
- the predetermined threshold is predetermined, such as one.
- step S704 If an affirmative result is obtained in step S704, that is, the quantization parameter is not less than the predetermined threshold, the process returns to step 702 to repeat the training in step S702.
- step S704 if a negative result is obtained in step S704, that is, the quantization parameter is already sufficiently small, the training process is ended.
- the training objective is to reduce both the MSE and the quantization parameter ⁇ . And if the MSE is 0, the quantization parameter ⁇ is very large; and if the quantization parameter ⁇ is very small, the MSE is large. Therefore, during the training process, an appropriate compromise between the MSE and the quantization parameter ⁇ needs to be based on acceptable quantized compression levels and image quality.
- the image encoding, decoding device, and image processing system as described above with reference to FIGS. 4 through 6 can also be used for a display device according to an embodiment of the present disclosure.
- the display device according to an embodiment of the present disclosure may be any product or component having a display function such as a mobile phone, a tablet, a television, a display, or the like.
- an image encoding device, an image decoding device, an image processing system including the image encoding and decoding device, a training method for the image processing system, and a display device according to the present disclosure have been described with reference to the accompanying drawings.
- the image encoding device, the image decoding device, and the image processing system including the image encoding and decoding device according to the present disclosure improve the downscaling performance of the encoder and the performance and degree of freedom of the decoder by the new weight parameter, thereby further improving The performance of the overall system.
- the image processing system training method according to the present disclosure achieves higher compression performance by optimizing the compression efficiency of the overall system with different quantization parameters.
- the image encoding device, the image decoding device, the image processing system, and the modules involved therein may be implemented by a central processing unit (CPU), or each device, module, or the like may be digital signal processor (DSP), programmable. Microprocessor implementations such as arrays (FPGAs).
- CPU central processing unit
- DSP digital signal processor
- FPGAs field-programmable gate arrays
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Abstract
Description
Claims (26)
- 一种图像编码装置,包括:第一图像输入端,被配置为获取第一图像;多个第二图像输入端,被配置为获取多个第二图像;第一卷积神经网络模块,与所述多个第二图像输入端连接,被配置为对所述多个第二图像中的每个第二图像的特征更新,以得到相应的更新特征;图像叠加模块,与所述第一图像输入端、所述第一卷积神经网络模块连接,被配置为将所述多个第二图像中的每个第二图像的更新特征与所述第一图像叠加以生成叠加图像,将所述叠加图像输出;预测模块,与所述图像叠加模块连接,被配置为根据所述叠加图像,生成多个预测图像;以及图像差异获取模块,与所述多个第二图像输入端、所述预测模块连接,被配置为确定所述多个第二图像的每个与相应的所述预测图像的差异特征,将所述差异特征输出;输出接口,被配置为输出所述叠加图像和所述差异特征。
- 如权利要求1所述的图像编码装置,其中所述预测模块为第二卷积神经网络模块。
- 一种图像编码装置,包括:第一图像输入端,被配置为获取第一图像;多个第二图像输入端,被配置为获取多个第二图像;特征模块,与所述多个第二图像输入端连接,被配置为对所述多个第二图像中的每个图像的特征更新,以得到相应的更新特征;图像叠加模块,与所述第一图像输入端、所述特征模块连接,被配置为将所述多个第二图像中的每个图像的更新特征与所述第一图像叠加以生成叠加图像,将所述叠加图像输出;第二卷积神经网络模块,与所述图像叠加模块连接,被配置为根据所述叠加图像的每个,生成多个预测图像;以及图像差异获取模块,与所述多个第二图像输入端、所述预测模块连接,被配置为确定所述多个第二图像的每个与相应的所述预测图像的差异特征,将所 述差异特征输出;输出接口,被配置为输出所述叠加图像和所述差异特征。
- 如权利要求1或3所述的图像编码装置,还包括:拆分单元,与所述第一图像输入端和所述多个第二图像输入端相连,被配置为将输入的原始图像进行拆分,以获得所述第一图像和所述多个第二图像。
- 如权利要求1或3所述的图像编码装置,其中所述图像叠加模块将所述多个第二图像中的每个图像的更新特征与所述第一图像按照叠加权重叠加。
- 如权利要求5所述的图像编码装置,其中所述图像叠加模块被配置为将所述第一图像乘以第一权重参数以获得第一乘积,将所述更新特征乘以第二权重参数以获得第二乘积,叠加所述第一乘积和所述第二乘积以生成叠加图像;其中所述第一权重参数大于0,并且所述第一权重参数与所述第二权重参数的和为1。
- 如权利要求4所述的图像编码装置,其中所述拆分单元被配置为将原始图像拆分为2n个图像,第一图像的数量为1,第二图像的数量为2n-1,n为大于0的整数。
- 一种图像解码装置,包括:叠加图像输入端,被配置为接收叠加图像;差异特征输入端,被配置为接收差异特征;预测模块,与所述叠加图像输入端连接,被配置为根据所述叠加图像,生成多个预测图像;去差异模块,与所述差异特征输入端、所述预测模块连接,被配置为根据所述多个预测图像和所述差异特征,生成多个第二图像,将所述多个第二图像输出;第四卷积神经网络模块,与所述去差异模块连接,被配置为对所述多个第二图像中的每个图像更新,以得到相应的更新特征;以及图像去叠加模块,与所述叠加图像输入端、所述第四卷积神经网络模块连接,被配置为根据所述更新特征对所述叠加图像执行去叠加,以得到第一图像,并且将所述第一图像输出;输出端,被配置为输出所述多个第二图像和所述第一图像。
- 如权利要求8所述的图像解码装置,其中所述预测模块为第三卷积神 经网络模块。
- 一种图像解码装置,包括:叠加图像输入端,被配置为接收叠加图像;差异特征输入端,被配置为用于接收差异特征;第三卷积神经网络模块,与所述叠加图像输入端连接,被配置为用于根据所述叠加图像,生成多个预测图像;去差异模块,与所述差异特征输入端、所述第三卷积神经网络模块连接,被配置为根据所述多个预测图像中的每个图像和所述差异特征,生成多个第二图像,将所述多个第二图像输出;特征模块,与所述去差异模块连接,被配置为对所述多个第二图像中的每个图像更新,以得到相应的更新特征;以及图像去叠加模块,与所述叠加图像输入端、所述特征模块连接,被配置为根据所述更新特征对所述叠加图像执行去叠加,以得到第一图像,将所述第一图像输出;输出端,被配置为输出所述多个第二图像和所述第一图像。
- 如权利要求8或10所述的图像解码装置,其中还包括:拼接单元,与所述输出端相连,被配置为将所述第一图像和所述多个第二图像拼接以获得解码图像,并且通过输出接口输出所述解码图像。
- 如权利要求8或10所述的图像解码装置,其中所述图像去叠加模块被配置为根据所述更新特征及其叠加权重对所述叠加图像执行去叠加。
- 如权利要求12所述的图像解码装置,其中所述图像去叠加模块被配置为将所述更新特征乘以第二权重参数以获得第二乘积,从所述叠加图像去除所述第二乘积以获得第一乘积,将所述第一乘积除以第一权重参数以获得所述第一图像;其中所述第一权重参数大于0,并且所述第一权重参数与所述第二权重参数的和为1。
- 一种图像处理系统,包括:图像编码装置,包括:第一图像输入端,被配置为获取第一图像;多个第二图像输入端,被配置为获取多个第二图像;第一卷积神经网络模块,与所述多个第二图像输入端连接,被配置为对所 述多个第二图像中的每个图像的特征更新,以得到相应的更新特征;图像叠加模块,与所述第一图像输入端、所述第一卷积神经网络模块连接,被配置为将所述多个第二图像中的每个图像的更新特征与所述第一图像叠加以生成叠加图像,将所述叠加图像输出;第一预测模块,与所述图像叠加模块连接,被配置为根据所述叠加图像的每个,生成多个预测图像;以及图像差异获取模块,与所述多个第二图像输入端、所述预测模块连接,被配置为确定所述多个第二图像的每个与相应的所述预测图像的差异特征,将所述差异特征输出;输出接口,被配置为输出所述叠加图像和所述差异特征;图像解码装置,包括:叠加图像输入端,被配置为接收所述叠加图像;差异特征输入端,被配置为接收所述差异特征;第二预测模块,与所述叠加图像输入端连接,被配置为根据所述叠加图像,生成多个预测图像;去差异模块,与所述差异特征输入端、所述预测模块连接,被配置为根据所述多个预测图像中的每个图像和所述差异特征,生成多个第四图像,将所述多个第四图像输出;第四卷积神经网络模块,与所述去差异模块连接,被配置为对所述多个第四图像更新,以得到相应的更新特征;以及图像去叠加模块,与所述叠加图像输入端、所述第四卷积神经网络模块连接,被配置为根据所述更新特征对所述叠加图像执行去叠加,以得到第三图像,将所述第三图像输出;输出端,被配置为输出所述多个第四图像和所述第三图像。
- 如权利要求14所述的图像处理系统,其中第一预测模块为第二卷积神经网络模块,第二预测模块为第三卷积神经网络模块。
- 一种图像处理系统,包括:图像编码装置,其具有第一图像输入端,被配置为获取第一图像;多个第二图像输入端,被配置为获取多个第二图像;第一特征模块,与所述多个第二图像输入端连接,被配置为对所述多个第二图像中的每个图像的特征更新,以得到相应的更新特征;图像叠加模块,与所述第一图像输入端、所述第一特征模块连接,被配置为将所述多个第二图像中的每个图像的更新特征与所述第一图像叠加以生成叠加图像,将所述叠加图像输出;第二卷积神经网络模块,与所述图像叠加模块连接,被配置为根据所述叠加图像的每个,生成多个预测图像;以及图像差异获取模块,与所述多个第二图像输入端、所述第二卷积神经网络模块连接,被配置为确定所述多个第二图像的每个与相应的所述预测图像的差异特征,将所述差异特征输出;输出接口,被配置为输出所述叠加图像和所述差异特征;图像解码装置,其具有叠加图像输入端,被配置为接收所述叠加图像;差异特征输入端,被配置为接收所述差异特征;第三卷积神经网络模块,与所述叠加图像输入端连接,被配置为根据所述叠加图像,生成多个预测图像;去差异模块,与所述差异特征输入端、所述第三卷积神经网络模块连接,被配置为根据所述多个预测图像中的每个图像和所述差异特征,生成多个第四图像,将所述多个第四图像输出;第二特征模块,与所述去差异模块连接,被配置为对所述多个第四图像更新,以得到相应的更新特征;以及图像去叠加模块,与所述叠加图像输入端、所述第二特征模块连接,被配置为根据所述更新特征对所述叠加图像执行去叠加,以得到第三图像,将所述第三图像输出;输出端,被配置为输出所述多个第四图像和所述第三图像。
- 如权利要求16所述的图像处理系统,其中所述第一特征模块为第一卷积神经网络模块,所述第二特征模块为第四卷积神经网络模块。
- 如权利要求14到17任一所述的图像处理系统,其中还包括量化装置,所述量化装置,与所述图像编码装置连接,被配置为接收从所述输出接口输出的所述叠加图像和所述差异特征,对所述叠加图像和所述差异特征执行量化处 理和逆量化处理,以生成量化叠加图像和量化差异特征;与所述图像解码装置,被配置为向所述图像解码装置的叠加图像输入端、差异特征输入端输出所述量化叠加图像、所述量化差异特征。
- 如权利要求18所述的图像处理系统,其中所述量化装置被配置为利用逆均匀阶梯量化函数InVUSQ对所述均匀阶梯量化函数USQ的输出q执行所述逆量化处理,以生成所述量化叠加图像和所述量化差异特征,其中InvUSQ(q)=sign(q)(|q|+0.5)δ。
- 一种图像编码方法,包括下述步骤:获取第一图像和多个第二图像;更新所述多个第二图像中的每个图像的特征以得到相应的更新特征;叠加所述第一图像和所述多个第二图像中的每个图像的更新特征生成叠加图像;根据所述叠加图像生成多个预测图像;确定所述第二图像中的每个图像与相应的预测图像的差异特征;输出所述叠加图像和所述差异特征;其中,所述更新和/或所述预测采用卷积神经网络。
- 如权利要求21所述的图像编码方法,包括下述步骤:将输入的原始图像拆分为所述第一图像和多个所述第二图像。
- 一种图像解码方法,包括下述步骤:接收叠加图像和差异特征;根据所述叠加图像生成多个预测图像;根据所述多个预测图像中的每个图像和所述差异特征,生成多个第二图像;对所述多个第二图像中的每个图像更新得到相应的更新特征;根据所述更新特征对所述叠加图像执行去叠加得到第一图像;输出所述多个第二图像和所述第一图像;其中,所述更新和/或所述预测采用卷积神经网络。
- 如权利要求23所述的图像解码方法,包括下述步骤:拼接所述第一图像和所述多个第二图像拼接以获得解码图像。
- 一种用于如权利要求14到17任一所述的图像处理系统的训练方法,包括:选择固定的所述量化参数;将训练图像输入所述图像处理系统,调整所述卷积神经网络模块中各卷积层中各滤波单元的权值,运行有限次迭代以使目标函数最优化;以及将所述量化参数减小预定值,如果所述量化参数不小于预定阈值,则重复使目标函数最优化的训练步骤,否则结束所述训练方法。
- 如权利要求25所述的训练方法,其中所述目标函数为:θ=argθminXMSE(X,OUTθ(X,δ))X代表输入的所述训练图像,OUT代表输出图像,MSE是输入的所述训练图像与所述输出图像之间的均方差函数。
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