Processing system for potential representation of image

Abstract

The invention discloses a processing system for potential representation of an image. The system comprises a potential representation verification unit 1, a super-prior information generation unit 2 and a super-prior quantization unit 3, the potential representation verification unit 1 is used for evaluating the quality of the potential representation, performing inverse transformation on the potential representation to restore the potential representation into an image, and comparing the image with an original image; the hyper-prior information generation unit 2 selects the most appropriate method to generate hyper-prior information according to the performance evaluation; a hyper-priori quantization unit 3 determines the most suitable quantization method according to the hyper-priori information generated by the hyper-priori information generation unit 2. According to the method, the defects in the prior art are overcome, and remarkable technical effects are achieved in the aspects of potential representation verification, super-prior information generation and quantization and the like. According to the technical effects, the accuracy and reliability of image potential representation processing are improved, the data storage and transmission cost is reduced, and a new thought and scheme are provided for development and application of the image processing technology.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology, and in particular relates to a processing system for the latent representation of images. Background Technology

[0002] Image latent representation refers to the feature representation obtained by transforming image data into a low-dimensional latent space through specific transformations or mappings. In today's data processing and analysis fields, latent representation, as a method of abstracting and compressing raw image data, has been widely used. Latent representation can reduce the dimensionality of data while preserving key features, thereby improving data processing efficiency. It plays an important role in many fields, such as image recognition and speech processing. However, there are still some shortcomings in the quality evaluation of latent representations and in the generation and quantization of prior information based on latent representations.

[0003] In terms of latent representation verification, most existing verification methods are limited to simple image reconstruction and comparison. This single verification method cannot comprehensively and deeply evaluate the quality of latent representations and may miss some important semantic information or loss of multimodal information in the latent representation.

[0004] In the generation of prior information, existing methods often employ a single analytical approach, lacking a systematic comparison of multiple analytical methods and the ability to select the appropriate method as needed. For example, using only simple statistical analysis methods may fail to fully uncover the complex features in the latent representation, while relying solely on deep learning methods may face problems such as high computational complexity and difficulties in model training. Furthermore, the lack of a mechanism for dynamically adjusting analytical methods makes them unsuitable for adapting to the real-time changes in latent representation data.

[0005] In the area of ​​prior quantization, current methods for quantifying prior information are often chosen blindly, failing to fully consider the multiple relevant dimensions inherent in the prior information itself, resulting in poor quantization performance. Furthermore, the setting of quantization parameters is often based on empirical values, lacking systematic parameter analysis and experimental optimization processes, leading to significant quantization errors, unsatisfactory data compression ratios, and an inability to meet the requirements of data accuracy and efficient storage and transmission in practical applications.

[0006] In summary, existing latent representation processing techniques have certain shortcomings in various key aspects, and a more comprehensive, systematic, and efficient technical solution is urgently needed to address these issues. Summary of the Invention

[0007] The present invention aims to solve the following technical problems.

[0008] I. Technical Problems in Latent Representation Verification. Most existing latent representation verification methods are limited to simple image reconstruction and comparison, which has significant limitations. First, it cannot comprehensively and deeply evaluate the quality of latent representations. Latent representations may contain rich semantic and multimodal information, while simple image reconstruction and comparison often only reveals the preservation of some visual features, and may not effectively detect the loss of other key information. Second, a single verification method may be affected by subjective factors, leading to questions about the accuracy and reliability of the evaluation results. Therefore, the technical problem this invention aims to solve in latent representation verification is to provide a more comprehensive and objective verification method that can accurately evaluate the quality of latent representations and reveal key information that may be lost in the latent representations.

[0009] II. Technical Problems in Generating Prior Information. In the generation of prior information, existing methods often employ a single analytical approach, lacking systematic comparison and on-demand selection of multiple analytical methods. The limitation of this approach is that different analytical methods may be applicable to different types of latent representation data, and a single analytical method may fail to fully uncover the complex features in the latent representation. Furthermore, the lack of a mechanism for dynamically adjusting analytical methods also makes existing methods unable to adapt to the real-time changing characteristics of latent representation data. Therefore, the technical problem this invention aims to solve in generating prior information is to provide a method that can systematically compare and select analytical methods on demand to generate high-quality prior information and adapt to the real-time changes in latent representation data.

[0010] III. Technical Issues in Prior Quantization. Current methods for prior quantization suffer from problems such as blind selection of quantization methods and parameter settings based on empirical values. This leads to poor quantization results, unsatisfactory data compression ratios, and an inability to meet the requirements of data accuracy and efficient storage and transmission in practical applications. Specifically, blind selection of quantization methods may fail to fully utilize the multiple relevant dimensions of prior information, while empirically set parameters can result in significant quantization errors.

[0011] This invention addresses the shortcomings of existing technologies by providing a comprehensive and systematic image latent representation processing system to solve technical problems related to latent representation verification, prior information generation, and quantization. Specifically, this invention provides a processing system for image latent representations, comprising a latent representation verification unit 1, a prior information generation unit 2, and a prior quantization unit 3. The latent representation verification unit 1 evaluates the quality of the latent representation by performing an inverse transformation to restore the latent representation to an image and comparing it with the original image to determine whether the latent representation retains key information from the visual features of the image. The prior information generation unit 2, based on the latent representation verified by the latent representation verification unit 1, selects the most suitable method to generate prior information based on performance evaluation, extracting key features from the latent representation and performing dimensionality compression. The prior quantization unit 3, based on the prior information generated by the prior information generation unit 2, determines the most suitable quantization method and optimizes the quantization parameters through parameter analysis and experiments to achieve the quantization of the prior information.

[0012] Preferably, the latent representation verification unit 1 further includes an image inverse transformation unit 11 and an image comparison unit 12; the image inverse transformation unit 11 is used to restore the latent representation to an image through a preset inverse transformation algorithm; the image comparison unit 12 compares the image obtained by the inverse transformation with the original image.

[0013] Preferably, the prior information generation unit 2 further includes an analysis method research unit 21, a performance evaluation unit 22, and a method selection unit 23; the analysis method research unit 21 studies various analysis methods such as statistical analysis, machine learning, and deep learning; the performance evaluation unit 22 evaluates the performance of different analysis methods from multiple dimensions such as computational complexity, feature extraction capability, and adaptability to latent representation data; the method selection unit 23 selects the most suitable analysis method to generate prior information based on the evaluation results of the performance evaluation unit 22.

[0014] Preferably, the a priori quantization module 3 further includes a quantization method research unit 31, a quantization parameter determination unit 31, and a quantization error processing unit 33; the quantization method research unit 31 conducts research on different quantization methods, including scalar quantization and vector quantization; the quantization parameter determination unit 32 determines the quantization parameters through parameter analysis and experimental optimization; and the quantization error processing unit 33 uses various strategies to process the quantization errors that are unavoidable during the quantization process.

[0015] Preferably, the processing using the multiple strategies includes: a prediction-based error compensation strategy and a noise shaping strategy.

[0016] Preferably, the image comparison unit 12 in the latent representation verification module further includes a multi-scale comparison subunit 121 and a structural similarity evaluation subunit 122; the multi-scale comparison subunit 121 compares the image obtained by the inverse transform with the original image at different scales; the structural similarity evaluation subunit 122 uses a structural similarity index to evaluate the similarity between the image obtained by the inverse transform and the original image.

[0017] Preferably, the latent representation verification unit further includes a feature matching unit 13, which further includes a feature point detection subunit 131, a feature descriptor generation subunit 132, and a matching relationship determination subunit 133.

[0018] Preferably, the feature point detection subunit 131 uses multiple feature point detection algorithms to detect feature points in the inverse-transformed image and the original image; the feature descriptor generation subunit 132 generates feature descriptors for the detected feature points; and the matching relationship determination subunit 133 uses the generated feature descriptors to determine the matching relationship between the feature points in the inverse-transformed image and the original image.

[0019] Preferably, the prior information generation unit 2 further includes a prior information fusion unit 24, which fuses prior information from different sources or of different types.

[0020] Preferably, the image inverse transform unit 11 includes an inverse transform network 111, the generator part of the inverse transform network 111 adopts a multi-layer convolutional transposed neural network structure; starting from the latent representation as input, it first passes through a fully connected layer to map the latent representation vector to a low-resolution feature map; the feature map enters the convolutional transposed layer; each convolutional transposed layer consists of a convolutional transpose operation, a batch normalization operation and a ReLU activation function; the discriminator of the inverse transform network 111 adopts a multi-layer convolutional neural network structure.

[0021] This invention provides a comprehensive and systematic image latent representation processing system, overcoming the shortcomings of existing technologies and achieving significant technical effects in latent representation verification, prior information generation, and quantification. These technical effects not only improve the accuracy and reliability of image latent representation processing but also reduce data storage and transmission costs, providing new ideas and solutions for the development and application of image processing technology. Attached Figure Description

[0022] Figure 1 This is a logic diagram of a processing system for latent image representation in an embodiment of the present invention.

[0023] Figure 2 This is a logic diagram of a potential representation verification unit in an embodiment of the present invention.

[0024] Figure 3 This is a logic diagram of the prior information generation unit in an embodiment of the present invention.

[0025] Figure 4 This is a logic diagram of the advanced prior quantization module in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0028] In a first embodiment of the present invention, a processing system for latent representation of an image is provided, such as... Figure 1 As shown, the system comprises three parts: a latent representation verification unit 1, a priori information generation unit 2, and a priori quantization unit 3. These three units are connected in sequence. The output of the latent representation verification unit serves as the input of the priori information generation unit, and the output of the priori information generation unit serves as the input of the priori quantization unit.

[0029] Preferably, the latent representation verification unit 1 is used to evaluate the quality of the latent representation by performing an inverse transformation on the latent representation to restore it to an image and comparing it with the original image, so as to preliminarily determine from the perspective of the visual features of the image whether the latent representation retains the key information of the original image.

[0030] The super-prior information generation unit 2 is based on the validated latent representation. By studying various analysis methods such as statistical analysis, machine learning, and deep learning, it selects the most suitable method to generate super-prior information based on performance evaluation, so as to extract key features in the latent representation and perform dimensionality compression.

[0031] The advanced prior quantization unit 3 investigates different quantization methods based on the characteristics of the advanced prior information, comprehensively considers factors such as quantization error, computational complexity, and adaptability to advanced prior information, determines the most suitable quantization method, and sets quantization parameters through parameter analysis and experimental optimization to achieve effective quantization of advanced prior information.

[0032] In another embodiment of the invention, such as Figure 2 As shown, the latent representation verification unit 1 further includes an image inverse transformation unit 11 and an image comparison unit 12.

[0033] The image inverse transform unit 11 is used to restore the latent representation to an image using a preset inverse transform algorithm. In practical applications, different inverse transform algorithms may be required for different types of latent representations. For example, for latent representations generated based on convolutional neural networks (CNNs), algorithms such as transposed convolution may be used for image restoration. Preferably, the inverse transform algorithm is determined according to the generation method of the latent representation to ensure that the latent representation can be accurately restored to a comparable image.

[0034] Image comparison unit 12 compares the image obtained from the inverse transform with the original image. This comparison can employ various metrics, such as mean square error (MSE) and peak signal-to-noise ratio (PSNR). MSE reflects the average error between corresponding pixel values ​​in two images; its reasonable range is usually determined based on the specific application scenario. In general image quality assessment, a smaller MSE value indicates that the image quality is closer to the original image. PSNR, defined based on MSE, is a metric for measuring image quality. Generally, a higher PSNR value indicates better image quality. In most image application scenarios, a PSNR value greater than 30 dB indicates a better subjective perception of image quality by the human eye. These metrics are used to quantitatively evaluate the quality of the latent representation and determine whether the latent representation retains the key visual information of the original image.

[0035] In yet another embodiment of the invention, such as Figure 3 As shown, the prior information generation unit 2 further includes an analysis method research unit 21, a performance evaluation unit 22, and a method selection unit 23.

[0036] The Analytical Methods Research Unit 21 conducts in-depth research on various analytical methods, including statistical analysis, machine learning, and deep learning. For statistical analysis methods, such as Principal Component Analysis (PCA), it studies how PCA projects high-dimensional data into a low-dimensional space through linear transformations to reveal the main directions of change in the data, thereby extracting key features from the latent representation. In the PCA algorithm, the number of principal components is a key parameter, and its reasonable range usually depends on the dimensionality of the latent representation data and the amount of information to be retained. In this invention, the number of principal components is less than the original data dimensionality, and its specific value is determined based on the feature distribution of the actual data to ensure that dimensionality is effectively reduced while retaining most of the key information. Support Vector Machines (SVMs) are used to model the latent representation data and mine hidden patterns in the data. In SVM applications, the choice of kernel function is crucial; different kernel functions are suitable for different data distributions. For example, linear kernel functions are suitable for linearly separable data, while Gaussian kernel functions are suitable for more complex data distributions. Meanwhile, the penalty parameter C is also a key parameter, which controls the degree of penalty the model imposes on misclassified samples. The reasonable range of C usually needs to be determined by searching within a certain range through methods such as cross-validation. Generally, the value range is between 0.1 and 100, and the specific value depends on the characteristics of the data.

[0037] Performance evaluation unit 22 evaluates the performance of different analysis methods from multiple dimensions, including computational complexity, feature extraction capability, and adaptability to latent representation data. Computational complexity evaluation primarily considers the computational resources required by the algorithm when processing latent representation data, such as time and space complexity. Time complexity can be determined by analyzing the number of basic operations executed during algorithm execution, while space complexity focuses on the amount of memory space occupied by the algorithm during operation. Feature extraction capability evaluation can be measured by comparing the performance of features extracted by different methods in subsequent tasks. For example, the extracted features can be used in image classification tasks, and the effectiveness of the features can be evaluated by the classification accuracy. Adaptability to latent representation data evaluation mainly observes the performance stability of different methods when facing latent representation data of different types and distributions.

[0038] Based on the evaluation results of the performance evaluation unit 22, the method selection unit 23 comprehensively considers the characteristics of the latent representation data and the needs of the actual application scenario, and selects the most suitable analysis method to generate the prior information. For example, if the latent representation data has a clear linear structure and requires high computational complexity, principal component analysis may be selected; if the data distribution is complex and requires high accuracy in feature extraction, a deep learning-based method may be selected.

[0039] In yet another embodiment of the invention, such as Figure 4As shown, the advanced prior quantization module 3 further includes a quantization method research unit 31, a quantization parameter determination unit 31, and a quantization error processing unit 33.

[0040] The quantization method research unit 31 investigates different quantization methods, including scalar quantization and vector quantization. In scalar quantization, the quantization step size is a key parameter, determining the accuracy and data compression ratio after quantization. A smaller quantization step size results in higher accuracy but lower data compression; a larger quantization step size results in higher compression but lower accuracy. The appropriate range needs to be determined based on the dynamic range of the prior information and the required quantization accuracy. Preferably, this invention determines the appropriate quantization step size by statistically analyzing the prior information and considering the tolerance for accuracy in the actual application scenario. In vector quantization, codebook design is crucial; the number and distribution of codewords in the codebook affect the quantization effect. More codewords result in higher accuracy but also increased computational complexity. The appropriate range requires a trade-off between quantization accuracy and computational complexity. Preferably, this invention determines the appropriate number of codewords through experiments. Simultaneously, the codebook training method is also critical. Common training methods include the LBG algorithm, and research is needed on how to select a suitable training method based on the characteristics of the prior information to generate an efficient codebook.

[0041] The quantization parameter determination unit 32 determines the quantization parameters through parameter analysis and experimental optimization, taking into account factors such as quantization error, computational complexity, and adaptability to prior information. For example, for scalar quantization, when determining the quantization step size, a preliminary range of quantization step size is estimated based on the statistical characteristics of prior information. Then, within this range, experiments are conducted to compare the quantization error and computational complexity under different quantization step sizes. This invention uses metrics such as mean squared error (MSE) to measure quantization error; the smaller the MSE value, the smaller the quantization error, meaning the quantized data is closer to the original data. Simultaneously, the time required for algorithm execution under different quantization step sizes is recorded to assess computational complexity. Through multiple experiments, a quantization step size that achieves a good balance between quantization error and computational complexity while meeting the requirements for adaptability to prior information is found. For vector quantization, when determining codebook-related parameters, an initial range of codeword counts and a codebook training method are first set. By changing the number of codewords, different codebooks are generated using the selected training method, and the prior information is quantized. Similarly, quantization error was evaluated using metrics such as MSE, and computational complexity was assessed by analyzing the memory space and runtime consumed during algorithm execution. After multiple rounds of experiments and analysis, codebook parameters that could achieve optimal quantization performance under given conditions were determined, including the number of codewords and the codebook training method.

[0042] The quantization error processing unit 33 employs multiple strategies to handle the unavoidable quantization errors generated during the quantization process. One strategy is a prediction-based error compensation method, which utilizes the temporal or spatial correlation of prior information to predict the error of the current quantization value. For example, if the prior information has a certain continuity in the time series, the quantization error at the current moment can be predicted based on the quantization error at the previous moment and the changing trend of the prior information at the current moment. Then, the quantization value is compensated based on the predicted error to reduce the impact of the quantization error on subsequent processing. Another strategy is to use noise shaping techniques to distribute the quantization error across different frequency bands in the frequency domain. By designing appropriate filters, the quantization error is distributed more in frequency bands that are less sensitive to the human eye or subsequent processing algorithms, and less in sensitive frequency bands. For example, in the quantization of image-related prior information, the human eye is more sensitive to low-frequency information; therefore, noise shaping techniques can be used to distribute the quantization error more widely to high-frequency bands, thereby visually reducing the impact of the quantization error.

[0043] In yet another embodiment of the invention, such as Figure 2 As shown, the image comparison unit 12 in the latent representation verification module further includes a multi-scale comparison subunit 121 and a structural similarity evaluation subunit 122.

[0044] The multi-scale contrast subunit 121 compares the images obtained from the inverse transform at different scales with the original image. In practical applications, images contain information at different scales, from global large-scale features to local small-scale details. Through multi-scale contrast, the quality of the latent representation can be evaluated more comprehensively. In specific implementations, methods such as Gaussian pyramids or Laplacian pyramids can be used to decompose the image at multiple scales.

[0045] Taking the Gaussian pyramid as an example, a Gaussian pyramid is first constructed for both the original image and the image obtained through inverse transformation. The Gaussian pyramid obtains image versions at different scales by performing low-pass filtering and downsampling operations. In each layer, the image size gradually decreases while preserving the main features at the corresponding scale. Then, images at different scales are compared, for example, by calculating the difference between corresponding pixels at different scales. This allows analysis of the degree to which the latent representation retains information from the original image at different scales. The range of different scales can be determined based on the image resolution and practical application requirements. Generally, it can start from the original image size and downsample in powers of 2 to obtain 2-5 images at different scales for comparative analysis.

[0046] The structural similarity evaluation subunit 122 uses the Structural Similarity Index (SSIM) to evaluate the similarity between the inverse-transformed image and the original image. SSIM considers the brightness, contrast, and structural information of the image, which better aligns with human perception of image similarity. Its calculation process includes calculating the similarity of the brightness, contrast, and structural components of the original and inverse-transformed images respectively, and then weighting and combining these three components to obtain the final SSIM value.

[0047] In practical applications, the weights need to be adjusted according to specific circumstances. For example, in applications where more attention is paid to image structural information, the weights of structural components can be appropriately increased. The SSIM value ranges from -1 to 1; the closer the value is to 1, the more similar the two images are. Generally, when the SSIM value is greater than 0.8, the image obtained by the inverse transform can be considered to have high structural and visual similarity to the original image, indicating that the latent representation has well preserved the key information of the original image. By combining multi-scale comparison and structural similarity evaluation, the effectiveness of the latent representation verification module for image latent representation can be evaluated more comprehensively and accurately.

[0048] In another embodiment of the present invention, the latent representation verification unit further includes a feature matching unit 13, which further includes a feature point detection subunit 131, a feature descriptor generation subunit 132, and a matching relationship determination subunit 133.

[0049] The feature point detection subunit 131 uses a variety of feature point detection algorithms to detect feature points in the image obtained by inverse transformation and the original image.

[0050] Feature descriptor generation subunit 132 generates feature descriptors for detected feature points. If the SIFT algorithm is used, after determining the location and scale of the feature point, the gradient direction histogram (HHMA) within the neighborhood of the feature point is calculated to generate the feature descriptor. Each feature descriptor is typically represented by a 128-dimensional vector. During its calculation, parameters such as the size of the neighborhood window and the number of groups in the HHMA are parameters that affect the performance of the feature descriptor. The neighborhood window size is typically 16×16 pixels, and the number of HHMA groups is typically 8. For the SURF algorithm, its feature descriptors are generated based on the Haar wavelet response within the neighborhood of the feature point, and the descriptor dimension is typically 64. During descriptor generation, parameters such as the size and direction of the Haar wavelet affect the uniqueness and stability of the descriptor. The ORB algorithm uses the BRIEF descriptor, which generates a binary descriptor by comparing pixel pairs within the neighborhood of the feature point. The length of the descriptor is typically 256 bits or 512 bits. During the generation of the BRIEF descriptor, the sampling mode of the pixel pairs is crucial; different sampling modes affect the performance and computational efficiency of the descriptor.

[0051] The matching relationship determination subunit 133 uses the generated feature descriptors to determine the matching relationship between feature points in the inverse-transformed image and the original image using algorithms such as nearest neighbor matching or KD-tree search. Taking the nearest neighbor matching algorithm as an example, for each feature descriptor in the inverse-transformed image, the descriptor with the closest Euclidean distance in the feature descriptor set of the original image is searched as the matching point. To improve the accuracy of matching, a distance threshold can be set; only when the nearest neighbor distance is less than this threshold are the two feature points considered to be matched. The value of the distance threshold needs to be determined based on the distribution of feature descriptors and noise levels, and is generally selected experimentally between 0.5 and 0.8 times the average distance of feature descriptors.

[0052] In another embodiment of the present invention, the prior information generation unit 2 further includes a prior information fusion unit 24. This unit is designed to fuse prior information from different sources or of different types to improve the guidance effect on potential representations.

[0053] In practical applications, prior information may come from multiple sources, such as semantic labels of images, scene context information, and other related auxiliary data. The prior information fusion unit 24 first preprocesses the prior information from different sources. For semantic label information, encoding may be required to convert it into a vector form suitable for fusion with the latent representation. Assuming the semantic labels are image category labels obtained through a classification task, such as "landscape" or "person," one-hot encoding can be used to convert these category labels into vectors. Each category corresponds to one dimension in the vector; when an image belongs to a certain category, the value of that dimension is 1, and the values ​​of the other dimensions are 0.

[0054] Scene context information may include the geographic location and time of the scene where the image is located. If the scene context includes geographic location information, such as latitude and longitude, it can be normalized so that its value is within the range of [0,1], so that it can be fused with other prior information on the same scale. Time information, such as the shooting time, can be converted into a timestamp and further normalized.

[0055] The prior information fusion unit 24 employs multiple fusion strategies to fuse different types of prior information. One fusion strategy is weighted summation. For the preprocessed semantic label vector and scene context vector, a weight is assigned to each vector, and then they are summed according to their weights to obtain the fused prior information vector. The weight allocation can be determined based on the importance of different prior information in the specific application. For example, in an application where image classification is the primary goal, semantic label information may be more important, so a larger weight, such as 0.6, can be assigned to the semantic label vector, while the scene context vector weight is set to 0.4. Another fusion strategy is based on neural network fusion. A small neural network is constructed, taking different types of prior information as input, and outputting the fused prior information after processing through several layers of the neural network. For example, a fully connected neural network with one hidden layer can be constructed. The number of neurons in the hidden layer can be adjusted according to the dimension of the input prior information, generally set to 0.5-1 times the sum of the input dimensions. The input layer receives the preprocessed semantic label vector and scene context vector. After being processed by the linear transformation and activation function such as ReLU in the hidden layer, the fused hyperprior information vector is obtained in the output layer.

[0056] The fused prior information then interacts with the latent representation. The prior information fusion unit 24 can combine the fused prior information with the latent representation through element-wise addition, concatenation, etc. If element-wise addition is used, the corresponding elements of the fused prior information vector and the latent representation vector are added, allowing the latent representation to absorb the knowledge contained in the prior information. This method can inject additional information into the latent representation without changing its dimensionality. If concatenation is used, the fused prior information vector and the latent representation vector are concatenated in dimension, resulting in a vector with a larger dimension. In this case, subsequent processing modules may need to perform dimensionality reduction or feature extraction operations on the concatenated vector to meet the needs of subsequent tasks. For example, Principal Component Analysis (PCA) can be used to reduce the dimensionality of the concatenated vector, retaining principal components and removing redundant information. The dimensionality after dimensionality reduction can be determined according to specific task requirements, generally between 0.3 and 0.8 times the original dimension.

[0057] By effectively fusing different prior information and reasonably interacting with latent representations through the advanced prior information fusion unit 24, the generation of latent representations can be guided more comprehensively and accurately, further improving the accuracy and reliability of the latent representation verification module in verifying latent representations.

[0058] In another embodiment of the present invention, the image inverse transform unit 11 includes an inverse transform network 111, which employs an improved generative adversarial network (GAN) structure. In this embodiment, the generator part employs a multi-layer convolutional transposed neural network structure. Starting with the latent representation as input, it first passes through a fully connected layer to map the latent representation vector to a low-resolution feature map. Assuming the latent representation is a 128-dimensional vector, the fully connected layer can map it to a 4×4×512 feature map. The weight matrix dimension of the fully connected layer is 128×(4×4×512), and the bias vector dimension is 4×4×512. Next, the feature map enters a series of convolutional transposed layers. Each convolutional transposed layer consists of a convolutional transpose operation, a batch normalization operation, and a ReLU activation function. Taking the first convolutional transposed layer as an example, the convolutional transpose kernel size can be set to 4×4, the stride is 2, and the padding is 1. The convolutional transpose operation transforms the input 4×4×512 feature map into an 8×8×256 feature map. Batch normalization normalizes the feature map after convolution transpose. Its normalization parameters γ and β are used to scale and translate the normalized data, respectively. The initial value of γ can be set to 1, and the initial value of β can be set to 0.

[0059] After batch normalization, the ReLU activation function is used to set values ​​less than 0 in the feature map to 0, enhancing the non-linear expressive power of the features. Subsequent convolutional transpose layers repeat similar operations, gradually increasing the resolution of the feature map to a size close to that of the original image. For example, the second convolutional transpose layer transforms an 8×8×256 feature map into a 16×16×128 feature map, the third convolutional transpose layer transforms a 16×16×128 feature map into a 32×32×64 feature map, and so on, until an inverse-transformed image with the same size as the original image is generated. The discriminator also uses a multi-layer convolutional neural network structure. It takes the original image and the inverse-transformed image generated by the generator as input. Taking a feature map output from an intermediate layer as an example, assuming the feature map size is 64×64×128, firstly, the feature map is compressed into a 1×1×128 vector through a global average pooling operation. Then, this vector is passed through two fully connected layers, one with an output dimension of 128 / 16 and the other with the same dimension. The outputs of these two fully connected layers are then summed and activated by a ReLU function, before being passed through a fully connected layer to output an attention weight vector with a dimension of 128. This attention weight vector is then reshaped to have the same dimension as the original feature map, 64×64×128. Finally, the attention weight vector is element-wise multiplied with the original feature map to obtain the feature map processed by the attention mechanism. In this way, the discriminator can pay more attention to important local features in the image, improving its ability to distinguish between generated and real images.

[0060] When training the improved GAN inverse transform network111, a multi-scale adversarial loss function is employed. Traditional GAN ​​loss functions only consider the differences between generated and real images at the global scale, while the multi-scale adversarial loss function calculates the adversarial loss between generated and real images at multiple different resolution scales. Specifically, the adversarial loss is calculated separately on the feature maps output from different layers of the discriminator.

[0061] In another embodiment of the present invention, when verifying the latent representation, the latent representation verification unit 1, in addition to considering the feature point matching relationship between the inverse-transformed image and the original image, also introduces semantic information verification. First, semantic segmentation is performed on the original image and the inverse-transformed image respectively. A deep learning-based semantic segmentation network, such as the U-Net network structure, is adopted. The U-Net network consists of an encoder, a decoder, and skip connections. The encoder part consists of multiple convolutional layers and pooling layers, used to downsample the input image and extract features at different levels. For example, the first convolutional layer uses a 3×3 convolutional kernel with a stride of 1 and padding of 1, performing a convolution operation on an input image of size 256×256×3, and outputting a feature map of size 256×256×64. Then, a 2×2 max pooling layer with a stride of 2 is used to reduce the feature map size to 128×128×64. Subsequent convolutional layers and pooling layers repeat similar operations, gradually reducing the resolution of the feature map and increasing the number of channels. The decoder section is symmetrical to the encoder and consists of multiple convolutional transpose layers and upsampling layers. It upsamples the low-resolution feature map output from the encoder to restore the original image size. In each upsampling step of the decoder, the feature map from the corresponding layer of the encoder is concatenated with the upsampled feature map via skip connections. For example, in the first upsampling layer of the decoder, the 128×128×64 feature map output from the corresponding layer of the encoder is concatenated with the upsampled 128×128×64 feature map along the channel dimension to obtain a 128×128×128 feature map. Then, a convolutional layer is used to perform feature fusion on the concatenated feature map. A semantic segmentation network segments the original image and the inverse-transformed image into different semantic categories, such as "sky," "buildings," and "roads." For each semantic category, its distribution in the original and inverse-transformed images is calculated. For example, statistics such as the area percentage and centroid position of each semantic category in the image are calculated. The effectiveness of the latent representation is verified by comparing these statistics of the same semantic category in the original and inverse-transformed images.

[0062] Furthermore, to improve the accuracy of semantic segmentation, a multimodal data augmentation strategy is employed during the training of the semantic segmentation network. In addition to traditional data augmentation methods such as rotation, flipping, and scaling, a generative adversarial network (GAN)-based data augmentation is introduced. Specifically, a GAN is constructed where the generator takes a noise vector and the semantic labels of the original image as input to generate an augmented image. The discriminator then determines whether the input image is a real image or an augmented image generated by the generator. During training, the generator and discriminator train adversarially, enabling the generator to generate augmented images with similar semantic structures to real images. Simultaneously, to make the generated augmented images more consistent with real-world application scenarios, a semantic consistency constraint is added to the generator's loss function. This involves calculating the difference between the semantic labels of the segmented image and the semantic labels of the original image, and incorporating this difference as a loss term into the generator's loss function. This multimodal data augmentation strategy expands the diversity of training data, improves the adaptability and accuracy of the semantic segmentation network to images in different scenarios, and ultimately enhances the reliability of the latent representation verification module's semantic information-based verification. In another embodiment of the present invention, considering the impact of changes in images under different lighting conditions on the verification of potential representations, an illumination normalization module is introduced.

[0063] In different embodiments of the present invention, through the aforementioned series of technical improvements and optimizations, the reliability, accuracy, real-time performance, and stability of the latent representation verification module based on semantic information verification are comprehensively improved from multiple aspects, including image illumination processing, adaptive adjustment of the semantic segmentation network, model compression acceleration, anomaly detection and recovery, security and privacy protection, and system scalability. This makes the technical solution more competitive and has broad application prospects in various image-related application fields. The specific technical effects of the present invention are as follows.

[0064] Technical advantages in latent representation verification. The latent representation verification unit of this invention enables a comprehensive and objective evaluation of the quality of latent representations. This unit employs image inverse transform and image comparison techniques to restore the latent representation to an image and compare it with the original image, thereby determining whether the latent representation retains the key information of the original image. Furthermore, the image comparison unit further includes a multi-scale comparison subunit, a structural similarity evaluation subunit, and a feature matching unit. These subunits can more comprehensively evaluate the quality of the latent representation from different angles and levels, ensuring the accuracy and reliability of the latent representation.

[0065] Technical advantages in generating prior information. The prior information generation unit of this invention can generate prior information based on the validated latent representation and by selecting the most suitable method according to performance evaluation. This unit includes an analysis method research unit, a performance evaluation unit, and a method selection unit. Through systematic comparison and on-demand selection of analysis methods, it can fully explore the complex features in the latent representation and generate high-quality prior information. In addition, it also includes a prior information fusion unit for fusing prior information from different sources or types, further improving the richness and accuracy of the prior information.

[0066] Technical advantages of prior quantization: The prior quantization unit of this invention can determine the most suitable quantization method based on generated prior information and optimize quantization parameters through parameter analysis and experimentation. This unit includes a quantization method research unit, a quantization parameter determination unit, and a quantization error processing unit. By researching different quantization methods, determining quantization parameters, and employing various strategies to handle quantization errors, effective quantization processing can be achieved. This not only reduces data storage and transmission costs but also ensures data accuracy and efficiency, meeting the requirements of practical applications for accurate data storage and efficient transmission.

[0067] In summary, this invention addresses the shortcomings of existing technologies by providing a comprehensive and systematic image latent representation processing system, achieving significant technical effects in latent representation verification, prior information generation, and quantification. These effects not only improve the accuracy and reliability of image latent representation processing but also reduce data storage and transmission costs, providing new ideas and solutions for the development and application of image processing technology.

[0068] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0069] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A processing system for latent representation of images, characterized in that: The system includes a latent representation verification unit (1), a priori information generation unit (2), and a priori quantization unit (3). The latent representation verification unit (1) is used to evaluate the quality of the latent representation by performing an inverse transformation on the latent representation to restore it to an image and comparing it with the original image. From the perspective of the visual features of the image, it judges whether the latent representation retains the key information of the original image. The priori information generation unit (2) selects the most suitable method to generate priori information based on the latent representation verified by the latent representation verification unit (1) and the performance evaluation, so as to extract the key features in the latent representation and perform dimensionality compression. The priori quantization unit (3) determines the most suitable quantization method based on the priori information generated by the priori information generation unit (2) and sets the quantization parameters through parameter analysis and experimental optimization to realize the quantization of the priori information.

2. The image latent representation processing system as described in claim 1, characterized in that: The latent representation verification unit (1) further includes an image inverse transformation unit (11) and an image comparison unit (12); the image inverse transformation unit (11) is used to restore the latent representation to an image through a preset inverse transformation algorithm; the image comparison unit (12) compares the image obtained by the inverse transformation with the original image.

3. The image latent representation processing system as described in claim 2, characterized in that: The prior information generation unit (2) further includes an analysis method research unit (21), a performance evaluation unit (22), and a method selection unit (23); the analysis method research unit (21) studies various analysis methods such as statistical analysis, machine learning, and deep learning; the performance evaluation unit (22) evaluates the performance of different analysis methods from multiple dimensions such as computational complexity, feature extraction capability, and adaptability to potential representation data; the method selection unit (23) selects the most suitable analysis method to generate prior information based on the evaluation results of the performance evaluation unit (22).

4. The image latent representation processing system as described in claim 3, characterized in that: The a priori quantization module (3) further includes a quantization method research unit (31), a quantization parameter determination unit (31), and a quantization error processing unit (33); the quantization method research unit (31) conducts research on different quantization methods, including scalar quantization and vector quantization; the quantization parameter determination unit (32) determines the quantization parameters through parameter analysis and experimental optimization; the quantization error processing unit (33) uses various strategies to process the quantization errors that are unavoidable during the quantization process.

5. The image latent representation processing system as described in claim 4, characterized in that: The various strategies employed include: a prediction-based error compensation strategy and a noise shaping strategy.

6. The image latent representation processing system as described in claim 5, characterized in that: The image comparison unit (12) in the latent representation verification module further includes a multi-scale comparison subunit (121) and a structural similarity evaluation subunit (122); the multi-scale comparison subunit (121) compares the image obtained by the inverse transformation with the original image at different scales; the structural similarity evaluation subunit (122) uses a structural similarity index to evaluate the similarity between the image obtained by the inverse transformation and the original image.

7. The image latent representation processing system as described in claim 6, characterized in that: The latent representation verification unit further includes a feature matching unit (13), which further includes a feature point detection subunit (131), a feature descriptor generation subunit (132), and a matching relationship determination subunit (133).

8. The image latent representation processing system as described in claim 7, characterized in that: The feature point detection subunit (131) uses a variety of feature point detection algorithms to detect feature points in the inverse transformation image and the original image; the feature descriptor generation subunit (132) generates feature descriptors for the detected feature points; and the matching relationship determination subunit (133) uses the generated feature descriptors to determine the matching relationship between the feature points of the inverse transformation image and the original image.

9. The image latent representation processing system as described in claim 8, characterized in that: The prior information generation unit (2) further includes a prior information fusion unit (24) for fusing prior information from different sources or of different types.

10. The image latent representation processing system as described in claim 9, characterized in that: The image inverse transform unit (11) includes an inverse transform network (111), the generator part of which adopts a multi-layer convolutional transposed neural network structure; starting from the latent representation as input, the latent representation vector is first mapped to a low-resolution feature map through a fully connected layer; the feature map enters the convolutional transposed layer; each convolutional transposed layer consists of a convolutional transpose operation, a batch normalization operation and a ReLU activation function; the discriminator of the inverse transform network (111) adopts a multi-layer convolutional neural network structure.

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