79 results about "Dct coefficient" patented technology

The invention discloses a rolling bearing signal reconstruction method fusing 5G LDPC and compressed sensing. The method comprises the steps of obtaining an original vibration signal of a rolling bearing; performing DCT on the original vibration signal to obtain a frequency domain vibration signal; performing weighting processing on the frequency domain vibration signal; setting a threshold value and a weight based on a DCT coefficient of the original vibration signal to generate a weighting matrix, and performing calculation according to the frequency domain vibration signal and the weighting matrix to obtain a weighted vibration signal; compressed sensing processing is carried out on the weighted vibration signal through an observation matrix, a weighted observation signal is obtained, and the observation matrix is realized through a 5G-NR-LDPC code; and after channel transmission is carried out on the weighted observation signal, a reconstructed vibration signal is obtained through signal reconstruction. According to the method, signal reconstruction with a better effect is realized on the basis of ensuring high compression of the vibration signal of the original rolling bearing.

The invention discloses a screen content video coding perception code rate control method and device based on DCT domain JND, and relates to the field of video coding, and the method comprises the steps: obtaining a screen content video, and obtaining a DCT coefficient of the video through discrete cosine transform; guiding frame-level and CTU-level target bit allocation by using the texture energy weight; obtaining a JND threshold value of the DCT domain according to a product based on the spatial contrast sensitivity threshold value, the brightness masking modulation factor and the contrast masking adjustment factor; and deducing a Lagrange multiplier combined with a JND regulation factor by using a JND threshold value to realize code rate control. According to the method, the JND model suitable for the screen content video is established in the DCT domain, bit distribution is guided through the texture energy weight, the new rate distortion model is established, the coding rate distortion performance can be improved, and the code rate control precision of the screen content video is improved.

The invention discloses a document image tampering detection method based on text aggregation and multi-frequency enhancement, and relates to the technical field of computer vision, image forensics and deep learning, and the method comprises the steps: obtaining a to-be-detected original RGB image, carrying out the multi-mode decomposition of the to-be-detected image, and obtaining a DCT coefficient graph, a corresponding quantization table, a high-frequency view and a low-frequency view; and inputting the original RGB image, the DCT coefficient graph, the corresponding quantization table, the high-frequency view and the low-frequency view into a document image tampering detection model for processing, and outputting a final detection result. The document image tampering detection model carries out feature dimension reduction and preliminary text aggregation through the vision-frequency fusion module, carries out coding and fusion through the multi-frequency feature extractor, generates comprehensive frequency features, carries out wavelet transform decoupling through the direction perception frequency decoupling enhancement module, and outputs tampered area masks based on the decoding prediction module. According to the invention, hidden tampering artifacts can be revealed more comprehensively.

Example techniques and devices for coding media data are described. An example device for encoding media data includes one or more memories configured to store the media data and one or more processors implemented in circuitry and coupled to the one or more memories. The one or more processors are configured to determine a hyperprior variable of a neural video coder based on the media data. The one or more processors are configured to apply a discrete cosine transform (DCT) to the hyperprior variable to generate DCT coefficients and encode the DCT coefficients.

The invention relates to the technical field of video noise reduction, in particular to an ultra-high-definition video noise reduction method. The method comprises the following steps: acquiring video data and noise variance; obtaining a gray threshold and a gradient threshold based on the gray value and the gradient value of each frame of image pixel point of the video data, obtaining the region of each frame of image, and then determining a growth criterion through the gray difference and the gradient difference to block the image; determining a gray feature weight and a texture feature weight based on the difference between the gray value and the gray limit value and the difference between the gradient value and the gradient limit value; improving the original DCT coefficient based on the feature weight and the texture feature weight to construct an exclusive dictionary, and denoising, decoding and reconstructing the image based on the exclusive dictionary; and synthesizing each frame of reconstructed image into a complete video. According to the invention, optimal noise reduction of the video image frame is realized and detail information of the video is reserved to the greatest extent.

The invention discloses a method for detecting tiny defects of a high-density integrated circuit packaging substrate. The method comprises the following steps: acquiring image data of the surface of the high-density integrated circuit packaging substrate; processing the acquired images in sequence by adopting a mean filtering method, morphological dilation and a texture operator; processing the image by adopting an image binarization processing technology to obtain a binarized image; dividing the binary image into a plurality of energy regions, and calculating an entropy difference through the divided energy regions; performing processing operation on the image data through a discrete cosine transform technology to obtain a DCT coefficient, dividing the DCT coefficient into a high-frequency component and a low-frequency component, and performing calculation to obtain a ratio of a low-frequency coefficient mean value to a high-frequency coefficient mean value; the feature extraction model extracts defect shape features based on the entropy difference and the ratio; comparing a similarity measurement index calculated according to the defect shape characteristics of the packaging substrate with a preset threshold value, and judging whether the packaging substrate has a shape defect or not; and precise detection of small defects of the high-density integrated circuit packaging substrate is realized.

The invention belongs to the technical field of office automation, and discloses a mail security detection method and system based on frequency domain and semantic analysis, electronic equipment and a computer readable storage medium. The method comprises the following steps: obtaining a to-be-detected mail, and carrying out preprocessing operation on an image in an attachment of the to-be-detected mail; performing block operation on the preprocessed image, and then performing DCT (Discrete Cosine Transformation) on each block to generate a corresponding DCT coefficient matrix; according to the DCT coefficient matrix, respectively calculating a high-frequency component entropy # imgabs0 # corresponding to each block; determining a steganography suspicion detection result of the mail according to the # imgabs 1 # value; based on the steganography suspicion detection result, SMTP flow semantic analysis of the mail is triggered, and a comprehensive risk score # imgabs2 is obtained; and based on the comprehensive risk score # imgabs3 #, triggering a linkage mechanism of multi-level alarm. According to the invention, a data stealing protection effect with high detection rate, low delay and strong adaptability on the mail is realized, and a reliable technical guarantee is provided for enterprise-level network security.

The invention discloses a pilot protection method, system and device for a new energy station sending-out line, and relates to the technical field of new energy station sending-out line protection.The method comprises the steps that transient fault current signals of the new energy station side and the system side on the two sides of a fault are decomposed into different frequency components through DCT; obtaining a two-dimensional DCT coefficient corresponding to the current data at each time point, and generating a binary image; the binary images are flattened into one-dimensional vectors to obtain respective perceptual hash values, the Hamming distance between the two perceptual hash values is calculated and compared to judge the similarity of waveforms, and pilot protection is carried out according to the similarity; according to the method, fault positioning of a sending-out line and accurate action of a protection device are achieved, the method is suitable for a network-forming type large-scale new energy sending-out system, and rapid and efficient fault processing is achieved.

In order to meet hierarchical, multi-user and differentiated application requirements in a complex environment, the invention provides an image grading security privacy protection method, which comprises the following steps of: partitioning a plaintext image to be encrypted, classifying DCT (Discrete Cosine Transform) coefficients of image blocks, and carrying out non-uniform compression by utilizing a compressed sensing technology; and in combination with adaptive threshold sparsification and a scrambling-diffusion encryption method, progressive image grading security privacy protection oriented to users with different grades of permissions is realized. Wherein the user with the low-level authority can only obtain the edge contour information of the image, the user with the middle-level authority can obtain the image with fuzzy texture details, and the user with the high-level authority can obtain the image which contains contour and detail information and has the best visual effect, so that hierarchical access control is realized.

A method for disparity estimation between digital images includes generating an interleaved image from two or more digital images with an offset between them, where the interleaved image is subdivided into a plurality of patches, computing discrete cosine transform (DCT) coefficients of each of the plurality of patches, computing, for each of the plurality of patches, a mean DCT descriptor from the DCT coefficients of each patch, and determining a disparity map from the mean DCT descriptor of each of the plurality of patches using a classifier. The disparity map is configured for real-time depth estimation from the two or more digital images.

The invention provides a residual DCT (Discrete Cosine Transformation) feature guided artifact learning module and an image tampering detection system, and the module comprises a DCT coefficient processing unit, a quantization table processing unit, a residual DCT calculation unit, a feature splicing unit and a neural convolution processing unit. The residual DCT calculation unit is used for performing repeated recompression on DCT coefficient matrix simulation JPEG compression to obtain DCT residual information and construct a residual DCT feature map on the basis of the DCT residual information, and the DCT coefficient processing unit is used for encoding the DCT coefficient matrix into DCT plane binary volume representation by using threshold truncation and One-Hot encoding, and then performing feature extraction and processing; the quantization table processing unit is used for expanding and processing a quantization table; the feature splicing unit is used for feature splicing; and the neural convolution processing unit is used for processing the spliced features to obtain final DCT features. According to the method, the positioning and identification capability of the tampered area in the double JPEG image with the same QF compressed twice is effectively improved.

The present invention relates to a method for detecting corrosion of boiler heating surfaces. Step 100: Parallelly decode the original video using a GPU to obtain grayscale images for each frame. Step 200: Divide the i-th frame image into N*N image blocks; parallelly perform DCT transformation on each image block using a GPU. Step 300: Parallelly convert the DCT coefficients of each image block into column vectors, and input each column vector into a trained improved BP neural network in sequence to obtain the determination result of whether there is corrosion in each image block. If there is corrosion in a certain image block, jump to Step 400; if there is no corrosion in all image blocks of the i-th frame image, let i = i + 1 and return to Step 200. Step 400: Input the i-th frame image into a trained improved Yolov5 model for corrosion detection to determine the specific location of corrosion, let i = i + 1 and return to Step 200. The accuracy of detecting the corrosion condition of the boiler heating surface is improved while reducing the amount of computation.

The present application relates to the technical field of image tampering detection and positioning, and relates to an image tampering detection and positioning method based on multi-domain fusion and dynamic quantization perception. The method comprises the following steps: S1: obtaining RGB pixel data, Y channel DCT coefficients and a quantization table of a to-be-detected image to obtain a to-be-detected data set; S2: performing multi-dimensional feature extraction on the to-be-detected data set to obtain a multi-dimensional feature set; S3: processing the multi-dimensional feature set to obtain a fused multi-dimensional feature tensor; S4: constructing a detection and positioning model based on multi-domain fusion and dynamic quantization perception; S5: performing two-stage training on the constructed detection and positioning model using a labeled image tampering sample data set; and S6: inputting the processed multi-dimensional feature tensor into the trained model to output a pixel-level tampering positioning result, thereby completing the image tampering detection and positioning. The present application has the advantages of significantly improved detection and positioning accuracy.

A method includes filtering an input image through a discrete cosine transform based noise filter (DCT-NF). The DCT-NF converts the input image from an original space into a perceptual space, applying gamma correction. The DCT-NF separates luminance channels of the input image from chroma channels of the input image. The DCT-NF divides the input image into overlapping patches and computes DCT transform of the patches. Each patch is a different partial portion of the input image. The DCT-NF suppresses patches that include an input DCT coefficient within a threshold range. The DCT-NF applies an inverse discrete cosine transform (IDCT) to the suppressed patches and remaining overlapping patches that include an input DCT coefficient outside the threshold range. The DCT-NF re-combines luminance and chroma channels of the IDCT-transformed patches. The DCT-NF generates a DCT noise-filtered output image by re-converting the IDCT-transformed patches to the original space by applying inverse gamma correction.

The disclosure provides a feature point detection-based anti-screen camera robust watermark embedding, extraction method and system, relates to the technical field of digital media copyright protection, and when a carrier image is photographed and watermark is extracted, an image correction algorithm based on PCA and SURF is proposed, the generated distortion can be well corrected, and the robustness of the screen camera watermark is indirectly ensured. The image after the screen camera can be corrected to restore the image to the state in the screen, and the selection of the feature area before and after the correction is also performed to improve the embedding effect of the watermark. In order to improve the robustness of the screen camera watermark, in the embedding of the screen camera watermark, the carrier image is subjected to DCT transformation, and then experiments are designed to select the DCT coefficients of the embedded watermark. Thus, after the watermark is embedded, the robustness is high, and the invisibility is also high.

This invention provides an image privacy protection method based on DCT compression and nonlinear dynamics. The method includes converting an RGB plaintext image to the YCbCr color space and sampling it; then performing Discrete Cosine Transform (DCT) and quantization; meticulously compressing and encoding the quantized DCT coefficients; and finally encrypting the reconstructed image matrix using a chaotic Rubik's Cube permutation algorithm based on nonlinear dynamics and an image filtering diffusion mechanism to obtain an encrypted image. This invention achieves high compression ratio, high-quality image recovery, and strong encryption security, effectively solving the privacy protection problem of image transmission in big data environments. By encrypting the compressed frequency domain information through chaotic Rubik's Cube and image filtering diffusion, a dynamic chaotic sequence strongly correlated with the plaintext is introduced to enhance the algorithm's resistance to cryptographic attacks, thereby forming an encrypted image with high compression ratio and high security.

The invention discloses a JPEG image batch steganography method and system based on experience security, and belongs to the technical field of information security. The method comprises the following steps: screening batch steganography carriers from a candidate JPEG image set based on experience security; calculating a non-zero DCT coefficient feature and a cost response curve D-alpha of each batch of steganography carriers; and calculating the optimal load rate of each batch of steganography carriers based on the non-zero DCT coefficient characteristics, the cost response curve D-alpha and the length of the load message, and executing steganography of the load message according to the optimal load rate. According to the method, the security of large-batch image steganography communication under an adaptive steganography algorithm can be improved.

The invention discloses an image recompression method and device, electronic equipment and a storage medium, and relates to the technical field of image coding, and the method comprises the steps: extracting metadata and an original DCT coefficient from an original image file, and the original image file is in a JPEG format; and determining a target deep learning model based on the metadata, processing the original DCT coefficient according to the target deep learning model, and generating a feature bit stream and a prediction image. And based on the prediction image, performing residual calculation on the original image file and the feature bit stream to obtain a residual bit stream. And combining and packaging the predicted image and the residual bit stream to obtain an image recompression file. By calculating the residual error between the predicted image and the original image, the focus of information compression is transferred from total image information to extremely small error information, so that the image can be reconstructed losslessly and completely at a decoding end according to a small amount of data and differential information, and the storage and transmission cost is effectively reduced while the image information completeness is ensured.

The invention discloses a color correction method and system, and belongs to the technical field of image processing, and the method comprises the steps: extracting a chromaticity quantization DCT coefficient, an initial chromaticity quantization table and a standard color card calibration matrix of a compressed image; wherein the chroma quantization DCT coefficient comprises a DCT coefficient of a blue chroma component and a DCT coefficient of a red chroma component; generating a chromaticity correction matrix according to the DCT coefficient of the blue chromaticity component of the compressed image, the DCT coefficient of the red chromaticity component of the compressed image and the standard color card calibration matrix; correcting the frequency domain chromaticity of the compressed image according to the chromaticity correction matrix; generating a final chroma quantization DCT coefficient according to the initial chroma quantization table and the chroma correction matrix; outputting a compressed image code stream according to the final chromaticity quantization DCT coefficient and the entropy coding table; according to the method, chromaticity correction is directly carried out in the frequency domain of the compressed image, quantization table optimization and chromaticity statistical feature modeling are combined, the problems of calculation complexity and distortion caused by decompression processing are avoided, and the color fidelity of the compressed image is improved.

The present invention proposes an image DCT steganography optimization method based on reinforcement learning, which relates to the field of image processing and information hiding technology. By dynamically selecting appropriate image blocks in the discrete cosine transform (DCT) domain for steganographic operation, the security of information embedding and image quality are optimized. The method first divides the image into several DCT coefficient blocks, and constructs a strategy network for image blocks in different regions. According to the DCT coefficients, energy distribution and image quality indicators of each image block, the system state is constructed, and the DCT coefficient embedding block for the steganographic operation is dynamically selected using a hierarchical reinforcement learning algorithm. While ensuring the image quality, it can not only make the embedded information more concealed, but also effectively avoid being easily identified in conventional detection algorithms.

The invention provides a JPEG (Joint Photographic Experts Group) compression artifact learning module and an image tampering detection system, and the JPEG compression artifact learning module comprises a DCT (Discrete Cosine Transform) coefficient processing unit, a quantization table processing unit, a feature splicing unit and a neural convolution processing unit, the DCT coefficient processing unit is used for coding a DCT coefficient matrix into DCT plane binary volume representation by using threshold truncation and One-Hot coding, and then performing feature extraction and processing to obtain a first feature map; the quantization table processing unit is used for expanding and processing the quantization table to obtain a second feature map; the feature splicing unit is used for splicing the first feature map and the second feature map; and the neural convolution processing unit is used for performing channel compression, normalization and activation on the spliced feature vectors to obtain final DCT features. According to the method, the CNN can directly learn a local JPEG compression artifact mode, and the tampering detection efficiency and precision are improved.

A method of encoding a video stream in a video encoder is provided that includes computing an offset into a transform matrix based on a transform block size, wherein a size of the transform matrix is larger than the transform block size, and wherein the transform matrix is one selected from a group consisting of a DCT transform matrix and an IDCT transform matrix, and transforming a residual block to generate a DCT coefficient block, wherein the offset is used to select elements of rows and columns of a DCT submatrix of the transform block size from the transform matrix.

A method and apparatus for perceptual bitrate control of screen content video coding based on Discrete Cosine Transform (DCT) domain JND is disclosed, relating to the field of video coding. The method includes: acquiring screen content video; obtaining DCT coefficients of the video through Discrete Cosine Transform; using texture energy weights to guide frame-level and CTU-level target bit allocation; obtaining a JND threshold in the DCT domain based on the product of a spatial contrast sensitivity threshold, a luminance masking modulation factor, and a contrast masking adjustment factor; and deriving a Lagrange multiplier for the JND adjustment factor using the JND threshold to achieve bitrate control. This invention establishes a JND model suitable for screen content video in the DCT domain, guides bit allocation through texture energy weights, and establishes a new rate-distortion model, which can improve coding rate-distortion performance and enhance the bitrate control accuracy of screen content video.

In one embodiment, an image feature is extracted that includes a lower-upper PSNR calculated between an original image and an image reduced (then re-scaled to an original resolution), an HOG (Histogram of Oriented Gradients) feature calculated on an image block extracted from the original image, and a DCT coefficient calculated on an image block extracted from the original image, and the DCT coefficient is also calculated on the image block. For DCT coefficients, only a subset at a high frequency is used. These image blocks are aggregated and serially connected before being fed to a neural network to predict QP (Quantization Parameter) switching values. If the QP value of the current picture is greater than the predicted QP switching value, RPR (Reference Picture Resampling) is applied to the current inter picture.

The application relates to the technical field of digital media processing, in particular to a robust steganography method and device for an image with a high quality factor and a large size, wherein the method comprises the following steps: decoding a carrier image to obtain first spatial domain pixel values of the carrier image; performing pre-scaling processing on a spatial domain image corresponding to the carrier image based on channel characteristics of a target network platform and obtaining discrete cosine transform (DCT) coefficients; embedding target secret information into the DCT coefficients to obtain initial carrier-cipher DCT coefficients, adjusting unstable coefficients of the initial carrier-cipher DCT coefficients, and obtaining final carrier-cipher DCT coefficients; transforming the final carrier-cipher DCT coefficients into a spatial domain to obtain second spatial domain pixel values, and modifying the first spatial domain pixel values according to the second spatial domain pixel values until an air domain image corresponding to the modified first spatial domain pixel values satisfies a first preset scaling condition; and compressing the air domain image corresponding to the modified first spatial domain pixel values to generate a final carrier-cipher image satisfying a preset size.

Example techniques and devices for coding media data are described. An example device for encoding media data includes one or more memories configured to store the media data and one or more processors implemented in circuitry and coupled to the one or more memories. The one or more processors are configured to determine a hyperprior variable of a neural video coder based on the media data. The one or more processors are configured to apply a discrete cosine transform (DCT) to the hyperprior variable to generate DCT coefficients and encode the DCT coefficients.

The invention provides a high-rise structure wind load spectrum identification method and device based on migration deep learning. The method comprises the following steps: calculating according to dynamic response time history data of a target structure under the action of a target wind load to obtain a corresponding observation response power spectrum matrix; performing discrete cosine transform decomposition on the wind speed spectrum of the target wind load to obtain a dimension-reduced DCT coefficient; constructing a deep learning model based on a physical guidance mechanism, taking the observation response power spectrum matrix as an input of the deep learning model, taking the DCT coefficient as an output, and enabling the deep learning model to learn a mapping relation between the observation response power spectrum matrix and the DCT coefficient through training; and outputting a corresponding DCT (Discrete Cosine Transform) coefficient by adopting the trained deep learning model based on the input observation response power spectrum matrix, and reconstructing a power spectrum matrix of the wind load according to the DCT coefficient. According to the technical scheme provided by the embodiment of the invention, the incidence relation between the response power spectrum and the load power spectrum can be effectively established, and the accuracy of wind load identification is ensured.

The invention discloses an automatic monitoring method and monitoring platform for a chemical dissolution process, and belongs to the field of computer vision, and the method comprises the steps: obtaining real-time image data of the chemical dissolution process; calculating a DCT coefficient and a DCT difference of the real-time image data, a gray scale difference value between an average gray scale value of the current frame and an average gray scale value of the reference value, and a maximum change rate of the gray scale; storing the data and a pre-acquired classification label as a data set; training a data set through a CatBoost model, and predicting the probability that a classification label is completely dissolved; when the probability that the classification label is completely dissolved is lower than a set threshold value, repeating the steps to re-train the CatBoost model until the probability that the classification label is completely dissolved reaches the set threshold value, and judging that the chemical medicine is completely dissolved; according to the invention, the efficiency and precision of chemical experiments can be improved, the difficulty and risk of experiment operation can be reduced, and the intelligent and automatic development of the chemical experiment technology can be promoted.

The invention belongs to the technical field of data processing, and discloses a color JPEG reversible watermark hiding method and system based on adaptive STC, and the method comprises the steps: obtaining a color JPEG image, decoding the color JPEG image, converting the color JPEG image into a Y color space, and extracting the DCT coefficients of the Y channel and the Y channel; according to the statistical characteristics of the DCT coefficients of all the channels, the embedding adaptability weight is calculated, and the embedding capacity is adaptively allocated to the three channels in combination with a preset channel weighting adjustment factor; a candidate set is selected according to the sequence of the three channels and the distributed embedding capacity, and zero value coefficients in the candidate set form a carrier sequence; the watermark to be hidden is embedded into the carrier sequence through STC coding, the secret-carrying JPEG image is generated, and high-capacity, low-distortion and completely reversible watermark hiding facing the color JPEG image is achieved.

The present invention relates to the field of industrial vision intelligent technology, and particularly relates to a potentiometer vision detection method for a potentiometer production line. This method uses the distance difference between the restored image corresponding to the maximum frequency in the DCT coefficient matrix and the boundary of the pin area to screen out abnormal pin areas. By comparing the overlapping degree of the pixel point areas between the image and the reference restored image, local suspected bending areas are screened out. Further, based on the distribution dispersion degree and the number of frequencies, the bending positioning area can be accurately screened out. The present invention is based on the discrete cosine transform, extracts the pin bending information by screening frequencies and performing transformation and restoration methods, and accurately locates the bending defect through step-by-step screening.