28 results about "Dct coefficient" patented technology

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.

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 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.

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.

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.

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.

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 application provides a point-sol gel fabric manufacturing method, equipment and medium, relates to the point-sol gel fabric manufacturing technical field, and comprises the following steps: dividing a target image into a plurality of sub-images; performing DCT transformation on each sub-image to obtain a corresponding DCT coefficient matrix of each sub-image; clustering all DCT coefficient matrices according to the characteristics of each DCT coefficient matrix; processing each DCT coefficient matrix in QA to obtain a processed DCT coefficient matrix cluster WA corresponding to QA; mapping the energy center corresponding to each DCT coefficient matrix in WA to the target image to obtain the centroid of each DCT coefficient matrix in QA; obtaining the centroid of a complete glue point composed of a defective glue point; determining whether the point-sol gel fabric to be detected is abnormal according to the centroid distribution of each glue point in the target image; and obtaining a final fabric; and the application can greatly reduce the data processing amount and significantly improve the detection efficiency.

The application discloses a kind of toxic image generation method and black box classification model fingerprint watermarking method, it is related to neural network model protection and computer vision technical field, the method includes using DWT-DCT-SVD method to diffuse fingerprint watermark into whole image, and DCT coefficient is encrypted.To solve the problem of image quantity imbalance and class imbalance, a toxic feature enhancement module is introduced to improve the reliability and fidelity of the watermark. By combining the toxic feature enhancement module with the original classification module, a fingerprint watermark with low embedding strength can be effectively obtained. The fingerprint watermark in the toxic image generated by this method has excellent concealment. Even in the case of a 1-bit difference in the fingerprint watermark, copyright verification can still be accurately performed. Moreover, it shows strong robustness against various model watermark attacks.

Noise reduction in images is provided by performing a noise reduction step on blocks of pixels within a video-processing pipeline. The noise reduction step consists of applying a discrete cosine transform (DCT) to the block of pixels, quantizing the resulting DCT coefficients, and performing an inverse of the DCT to the quantized coefficients. The output of that noise reduction step is a block of image pixels similar to the input pixels, but with significantly less image noise. Because the noise reduction step can be performed quickly on small blocks of pixels, the noise reduction can be performed in real-time in a video processing pipeline.

The invention discloses a decoding method and electronic equipment. Belongs to the technical field of image processing. The embodiment of the method comprises the following steps: reading a first Huffman code of a first coding unit from a coding bit stream of a JPEG (Joint Photographic Experts Group) format image; a first additional bit length corresponding to the first Huffman code is inquired from a corresponding relation table, and the corresponding relation table is used for storing the corresponding relation between the Huffman code and the additional bit length; reading additional bit data from the coded bit stream based on the first additional bit length, and determining a discrete cosine transform (DCT) coefficient based on the first additional bit length and the additional bit data; and generating image data of the JPEG format image based on the DCT coefficient.

This invention discloses a high-capacity robust video watermarking method resistant to geometric attacks, comprising: acquiring the YUV three channels of a video; applying DTCWT transform to the U channel to separate the coefficients in each sub-band; selecting a watermark embedding strategy, dividing the coefficients into blocks and applying DCT transform, modifying the DCT coefficients according to the watermark information to be embedded, and sequentially applying inverse DCT transform and inverse DTCWT transform to obtain the watermarked U channel; combining it with the Y and V channels of the original video and performing the inverse YUV conversion operation, finally obtaining the watermarked video through video encoding. The extraction process is based on DCT transform, and the watermark is extracted according to the embedding strategy. This invention achieves robust watermark embedding and extraction based on DTCWT and DCT, flexibly balancing the robustness of the watermark against geometric attacks with the capacity of the payload, and can be applied to multimedia platforms where video is the mainstream.

The invention discloses a compressed domain video key frame extraction method and system based on a rough set theory. The method comprises the following steps of: extracting DCT (Discrete Cosine Transform) coefficients of all I frames of a video stream in a compressed domain, acquiring DC (Direct Current) coefficients, constructing an information decision table, calculating an average difference value of the DC coefficients between adjacent frames by utilizing a rough set theory, and eliminating redundant frames through attribute reduction to obtain a primary key frame set; and then optimizing the primary set, initializing a reference frame and a subsequent frame, adopting an SURF algorithm to extract local feature points and calculate similarity, judging content redundancy according to a preset threshold value, and further screening out a final key frame set. According to the method, the key frame is efficiently and accurately extracted in the compressed domain, the processing speed and the content representativeness are considered, and the problems that a traditional method is large in calculation overhead and high in redundancy are effectively solved.

The invention discloses an image watermark processing method, device and equipment, a medium and a product, and relates to the technical field of computer data security. The method comprises the following steps: acquiring a to-be-embedded image, watermark text information and a quantization step size; performing channel separation on the to-be-embedded image, and determining a first channel image, a second channel image and a third channel image; the first channel image is an R channel image; the second channel image is a G channel image; the third channel image is a B channel image; performing discrete cosine transform on the third channel image to obtain a first DCT coefficient matrix; performing binary conversion on the watermark text information to obtain watermark coding information; embedding the watermark coding information into an intermediate frequency region of the first DCT coefficient matrix to obtain a second DCT coefficient matrix; performing inverse discrete cosine transform on the second DCT coefficient matrix to obtain a fourth channel image; and obtaining a target image based on the fourth channel image, the first channel image and the second channel image. Therefore, the watermark processing efficiency is improved.

The application provides a JPEG compression artifact learning module and an image tamper detection system, and the JPEG compression artifact learning module comprises a DCT coefficient processing unit, a quantization table processing unit, a feature splicing unit and a neural convolution processing unit, wherein the DCT coefficient processing unit is used for encoding a DCT coefficient matrix into a 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 a 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 vector to obtain a final DCT feature. The application enables the CNN to directly learn local JPEG compression artifact patterns, and improves tamper detection efficiency and accuracy.

The application belongs to the technical field of image coding, and specifically discloses a JPEG transcoding compression method based on multi-level context and prior information fusion, which comprises the following steps: based on the quantized DCT coefficients of a JPEG image, a first data group comprising U channel coefficients, V channel coefficients and Y1 channel coefficients and a second data group comprising Y2 channel coefficients, Y3 channel coefficients and Y4 channel coefficients are obtained; based on the first data group, the U channel coefficients, the V channel coefficients and the Y1 channel coefficients are sequentially subjected to prior information fusion coding through a checkerboard module and a channel-by-channel module to obtain a first code stream; based on the first data group and the second data group, the Y4 channel coefficients, the Y2 channel coefficients and the Y3 channel coefficients are sequentially subjected to coding through a step-by-step prior information fusion and a channel-by-channel module to obtain a second code stream; and based on the first code stream, the second code stream and image meta information, a secondary transcoding compression code stream is obtained. Through the application, the compression rate can be effectively 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.

The application relates to a general-purpose GPU-oriented JPEG decoding batch processing and double-buffer pipeline scheduling method, belongs to the field of heterogeneous computing and image processing, and comprises the following steps: obtaining code stream data of a to-be-decoded JPEG image; performing entropy decoding and inverse quantization on the code stream data by a CPU to obtain a plurality of DCT coefficient blocks; if the size of the to-be-decoded JPEG image is not smaller than a preset size threshold, the DCT coefficient blocks are cached in a ring buffer located in a host memory; the state of the ring buffer is continuously monitored; when a preset batch triggering condition is met and a GPU is detected to be available, the DCT coefficient blocks in the ring buffer are batch copied to GPU display memory; based on the DCT coefficient blocks in the GPU display memory, integer IDCT transformation and color space conversion calculation are performed by the GPU to obtain an original image corresponding to the to-be-decoded JPEG image, and the original image is read back to the host memory; wherein the double-buffer pipeline is adopted to perform batch copying of the DCT coefficient blocks, integer IDCT transformation, color space conversion calculation and reading back of the original image. Efficient real-time decoding is realized.

The application belongs to the technical field of image compression, and discloses a DCT coefficient processing circuit and method for image compression, which comprises an input module configured to receive DC coefficients and each AC coefficient of a pixel block; a coefficient code calculation module configured to calculate DC coefficient codes; a run statistics module comprising a reference pixel register and a run calculation unit; the run calculation unit is configured to: acquire reference AC coefficients and a reference run value in the reference pixel register; determine a target run value of a current pixel according to the reference AC coefficients and the reference run value; replace the reference AC coefficients with AC coefficients of the current pixel; replace the reference run value with the target run value of the current pixel; and a code length calculation module configured to calculate target binary codes and code lengths of the DC coefficients and each AC coefficient. When performing run encoding on the pixels, the application only needs to acquire AC coefficients and a run value of the previous pixel, thereby improving the efficiency of run encoding.

The application is a kind of JPEG image information hiding method based on direction correction, belonging to the field of information hiding technology, mainly including 1, preprocessing process; 2, secret information embedding; 3, secret information extraction three parts, when embedding secret information, first classify the coefficient pairs in the set C of embeddable coefficient pairs, and design direction correction rules for different coefficient pairs, then select the coefficient pairs to be embedded in the set C, judge the type of the coefficient pairs, and determine the position of the coefficient pairs in the reference matrix, then read 2-bit secret data in turn, and convert it into a quaternary number, compare the values of the reference data and the secret data, modify the coefficients under different conditions, until all the coefficient pairs embed the secret data; finally, entropy coding is performed on the quantized DCT coefficients embedded with secret data to obtain the steganographic JPEG image, the experimental results show that the method can effectively improve the performance of embedding capacity, image quality and file increment in the prior art.

The application discloses a privacy protection image retrieval method and system based on a ciphertext vision, belongs to the technical field of image retrieval, and aims to solve the technical problem that the existing privacy protection image retrieval method cannot simultaneously consider effective protection of image privacy content and high accuracy of image retrieval, and cannot meet current retrieval requirements. The method comprises the following steps: performing encryption processing on a to-be-queried image through a pre-defined reliable encryption algorithm to obtain an encrypted JPEG bit stream of the to-be-queried image; extracting DCT coefficients in the encrypted JPEG bit stream, and extracting corresponding image ciphertext features based on the DCT coefficients; wherein the image ciphertext features at least include a local length sequence feature and a global Huffman coding frequency feature; an unsupervised learning retrieval model based on a visual Transformer is constructed, and the unsupervised learning retrieval model is trained through a self-defined comprehensive loss function.

The application discloses a JPEG file secondary lossless coding method based on deep learning. First, the DCT quantization coefficient and related metadata are obtained by analyzing the JPEG file. Second, the DCT coefficient is grouped in the frequency domain and rearranged in the space to construct an autoregressive context. Then, the accurate probability distribution is generated through the deep learning network, and the mixed Gaussian and Laplace models are respectively used for different frequency bands. Then, the adaptive hybrid coding strategy is used to fuse the traditional run-length coding and the deep learning entropy coding. At the same time, the metadata such as the quantization table and the Huffman table are intelligently classified and efficiently compressed. Finally, the coding results are combined and packaged, and the decoding verification is realized to achieve the lossless reconstruction completely consistent with the original file. The method significantly improves the lossless compression rate of the JPEG file and has good adaptive ability.

A quantized audio signal is received. A decoder generates, based on the quantized audio signal and using a first dequantization operation, at least one local maximum discrete cosine transform (DCT) coefficient. The decoder determines at least one masking function based on the at least one DCT coefficient and generates a set of modified quantized values based on applying the at least one masking function to a set of quantized values of the quantized audio signal. The decoder generates a dequantized audio signal by dequantizing the set of modified quantized values. The decoder generates and outputs a reconstructed audio signal based on the dequantized audio signal.

This application belongs to the field of data processing technology and discloses a method and system for reversible watermark hiding of color JPEG based on adaptive STC. The method includes: acquiring a color JPEG image, decoding and converting it to the Y color space, and extracting the DCT coefficients of the Y, I, and II channels; calculating the embedding adaptation weights according to the statistical characteristics of the DCT coefficients of each channel, and adaptively allocating the embedding capacities of the three channels to the Y, I, and II channels in combination with a preset channel weighting adjustment factor; selecting a candidate set according to the allocated embedding capacity in the order of the three channels, and constructing a carrier sequence from the zero-value coefficients in the candidate set; embedding the watermark to be hidden into the carrier sequence through STC encoding to generate a watermark-hidden JPEG image, thereby achieving high-capacity, low-distortion, and completely reversible watermark hiding for color JPEG images.

This application relates to the field of video denoising technology, specifically to an ultra-high-definition video denoising method. The method includes: acquiring video data and noise variance; obtaining grayscale thresholds and gradient thresholds based on the grayscale values ​​and gradient values ​​of pixels in each frame of the video data, and acquiring the region of each frame image; then dividing the image into blocks by determining growth criteria based on grayscale differences and gradient differences; determining grayscale feature weights and texture feature weights based on the differences between grayscale values ​​and grayscale thresholds, and the differences between gradient values ​​and gradient thresholds; improving the original DCT coefficients based on the feature weights and texture feature weights to construct a dedicated dictionary; denoising, decoding, and reconstructing the image based on the dedicated dictionary; and synthesizing each reconstructed frame image into a complete video. This application achieves optimal denoising of video image frames while preserving the video's detailed information to the maximum extent.

The invention discloses a JPEG (Joint Photographic Experts Group) file secondary lossless coding method based on deep learning. The method comprises the following steps: firstly, analyzing a JPEG file to obtain a DCT quantization coefficient and related metadata; secondly, performing frequency domain grouping and spatial rearrangement on the DCT coefficients, and constructing an autoregression context; then, accurate probability distribution is generated through a deep learning network, and a Gaussian mixture model and a Laplacian mixture model are adopted for different frequency bands respectively; then, a self-adaptive hybrid coding strategy is adopted, and traditional run-length coding and deep learning entropy coding are fused; meanwhile, metadata such as a quantization table and a Huffman table are intelligently classified and efficiently compressed. And finally, coding results are combined and packaged, and lossless reconstruction which is completely consistent with that of the original file is realized through decoding verification. By means of the method, the lossless compression rate of the JPEG file is remarkably improved, and the good self-adaptive capacity is achieved.