60 results about "Ycbcr color space" patented technology

The invention discloses an endoscope multi-region semantic perception time domain fusion adaptive white balance algorithm. The algorithm comprises the following steps: acquiring an endoscope image; performing tissue region division on the endoscope image through a lightweight semantic segmentation network to obtain a plurality of region images; converting each regional image from an RGB space to a YCbCr color space, separating a brightness channel Y from chrominance channels Cb and Cr, and calculating a channel mean value of the chrominance channels Cb and Cr; calculating the average offset of the chrominance channels Cb and Cr according to the calculated channel average of the chrominance channels Cb and Cr; performing white point detection on each regional image to obtain an RGB three-channel mean value; combining the calculated mean offset of the chromaticity channels Cb and Cr, the RGB three-channel mean obtained by white point detection of each regional image and a statistical index to form a multi-dimensional regional color feature vector; and fusing historical frame features with the regional color feature vector to obtain a feature vector based on space-time fusion.

The invention belongs to the technical field of image data processing, and particularly discloses a low-illumination image dynamic enhancement method and device, a medium and a program product, and the method comprises the steps: obtaining a to-be-processed low-illumination image, converting the to-be-processed low-illumination image into a YCbCr color space, and extracting a brightness channel of the to-be-processed low-illumination image; the brightness channel is input into an adaptive enhancement intensity parameter prediction module, the adaptive enhancement intensity parameter prediction module learns illumination features of the brightness channel through a residual block and a channel attention mechanism, and an adaptive enhancement intensity eta value is output; and inputting the original RGB image, the brightness channel and the eta value into the low-illumination image enhancement network, realizing refined enhancement through multi-scale feature fusion, and outputting an enhanced image. According to the method, the self-adaptive enhancement intensity parameter prediction module and the low-illumination image enhancement network are fused, a new low-illumination image enhancement network is formed, automatic regression estimation and de-interactive image enhancement of the self-adaptive enhancement intensity eta value are realized, and the intelligence and robustness of image brightness recovery are effectively improved.

Systems and methods are provided for encoding and decoding images using differentiable JPEG compression, including converting images from RGB color space to YCbCr color space to obtain a luminance and chrominance channels, and applying chroma subsampling to the chrominance channels to reduce resolution. The YCbCr image is divided into pixel blocks and a DCT is performed on the pixel blocks to obtain DCT coefficients. DCT coefficients are quantized using a scaled quantization table to reduce precision, and quantized DCT coefficients are encoded using lossless entropy coding, forming a compressed JPEG file decoded by reversing the lossless entropy coding to obtain quantized DCT coefficients, which are dequantized using the scaled quantization table to restore the precision. The dequantized DCT coefficients are converted back to a spatial domain using an IDCT, the chrominance channels are upsampled to original resolution, and the YCbCr image is converted back to the RGB color space.

The invention relates to a local non-reference visual safety evaluation method and system, and belongs to the technical field of visual safety evaluation. The method comprises the following steps: carrying out image preprocessing, converting an encrypted image to be detected from an RGB space to a YCbCr color space, and segmenting the image into analysis units with different sizes by adopting a multi-scale sliding window and a pyramid scaling strategy aiming at a Y channel; performing encryption block positioning, calculating statistical characteristics such as a gray histogram, edge energy and texture entropy for each analysis unit, generating a binary encryption block mask, and realizing reliable positioning of a local encryption region; performing local security evaluation, taking each positioned encryption region as an independent evaluation unit, independently calculating a DCT high-frequency sub-band statistical offset parameter of each positioned encryption region, and fitting generalized Gaussian distribution through a moment estimation method; and according to the area proportion, the scores of the regions are aggregated to obtain the overall safety score of the whole graph, so that the evaluation accuracy is ensured, and the real-time and lightweight deployment requirements are met.

The invention discloses an image compression method and system based on edge gradient self-adaption, and the method comprises the steps: converting an input image into a YCbCr color space, calculating an edge gradient value of a brightness component, and employing iteration processing including a feedback mechanism: dividing an edge region and a non-edge region based on a current self-adaption edge gradient threshold value, the blocks are divided according to different sizes; dynamically updating an edge gradient threshold according to a block statistics result, and adaptively optimizing region division through multiple iterations; and finally, based on the optimized blocking result, processing the edge region and the non-edge region by adopting different quantization strategies, generating a quantization matrix, and then carrying out entropy coding to output a compressed code stream. According to the method, adaptive adjustment of compression parameters is realized through an iterative optimization mechanism, image edge and texture details are effectively reserved while a high compression rate is ensured, and the subjective visual quality of a compressed image is remarkably improved.

The invention relates to the technical field of information optics, in particular to a high-performance analysis triangular surface element color computer-generated hologram generation method, and aims to overcome the defects of an existing analysis surface element method for generating a computer-generated hologram. The method comprises the following steps: on the basis of primitive triangle frequency spectrum expression under three-dimensional affine transformation, generating a color computer-generated hologram with good reconstruction visual quality by adding orthogonal mapping textures and Blinn-Phong illumination model information into inclined triangle surface element amplitude; meanwhile, based on the RGB-YCbCr color space conversion relation, the texture image is converted from RGB to YCbCr color space so as to avoid repeated redundancy calculation of the triangular surface element frequency spectrum under each color, and the hologram generation efficiency is greatly improved while the quality of the reproduced image of the hologram is ensured.

The invention discloses a low-illumination image enhancement method combining multi-channel parallel attention and cross fusion, and relates to the field of photoelectric display. In order to solve the problems of high model complexity, brightness and color gamut information coupling, artifact fusion and the like in low-illumination image enhancement, the invention provides a lightweight low-illumination image enhancement network, brightness and chrominance information are separated in a YCbCr color space, brightness structure features are extracted through a lightweight denoising module and a multi-head attention mechanism, and a low-illumination image is obtained. And a multi-channel parallel attention module (MCPA) is designed to enhance the chromaticity context modeling capability. According to the invention, a brightness-guided cross fusion module (LCA) is introduced, and cooperative enhancement of the structure and the color is realized through joint channel-space attention optimization. According to the method, a relatively high image quality index is obtained on multi-class public low-illumination data, meanwhile, the parameter quantity and the calculation quantity are remarkably reduced, and the method is suitable for edge device deployment. According to the method, color cast and fusion artifacts can be effectively inhibited, and the consistency of structural details and colors is improved.

The invention discloses a low-illumination visible light and infrared image fusion method with color recovery capability, and belongs to the technical field of computer vision. The method comprises the following steps: designing a low-light enhancement loss function based on comparative learning, so that the color distribution of an enhanced visible light image is aligned with the color distribution of an ideal image; designing a low-light enhancement neural network based on a Mangbar structure, converting the low-illumination image into a YCbCr color space, decoupling an illumination component and a color component of the low-light image, and respectively implementing enhancement processing; a fusion network based on a Mangban structure is designed, modal complementary information is obtained through a mixed attention mechanism, the enhanced visible light image and the infrared image are fused, color information of the fused image can be effectively recovered, and the visual quality of the fused image is improved.

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 relates to the field of electric data processing, in particular to a data set distillation-oriented hidden backdoor attack method and device. The method comprises the following steps: acquiring an original clean training image sample S and a corresponding real label L, generating an implicit trigger t based on Gaussian distribution, and constructing a binary mask m; based on the binary mask m, the generated implicit trigger t is embedded into a brightness channel of a YCbCr color space of the original clean training image sample S in a linear superposition mode, and a poisoning sample Spoisoned is obtained; keeping the label of the poisoning sample Spoisoned consistent with the real label L of the original clean training image sample S; mixing the poisoning sample with the original clean training image sample to form a poisoning training data set Dpoison, and using the poisoning training data set Dpoison for data set distillation to obtain a synthetic data set Ssynth; according to the scheme, the filtering effect of data set distillation can be resisted, and high hiding is kept before and after distillation.

The application discloses a low-illumination image detail enhancement method, system, device and storage medium, and belongs to the technical field of image processing.The application converts an image from an RGB space to a YCbCr color space, separates a Y component representing overall brightness information of the image, so that the interference of chroma information of other channels on data processing is reduced in a subsequent processing process; meanwhile, the application can adjust appropriate gain parameters and fusion parameters according to specific illumination characteristics and scenes, so that the contrast and brightness of the low-illumination image are improved as a whole, the detail part of the low-illumination image is enhanced, and the picture quality of the low-illumination image is improved; finally, a linear mapping reconstruction technology is used to reconstruct the RGB color space, the color distortion problem possibly occurring in the reconstructed color space is improved, and the overall quality of the image is improved.

The invention relates to a depth data-containing image semantic transmission method, which comprises the following steps of: converting an RGBD image into a YCbCr color space, and dividing the YCbCr color space into four channels of brightness y, chromaticity u and v and depth d; in the feature extraction network, semantic feature extraction is performed on each input channel, yf, uf, vf and df are obtained, and yf, uf and vf are merged to obtain yuvf; a joint source channel encoder of a double-branch structure is constructed, one branch receives yuvf, the other branch receives df, and yuvj and dj are output after encoding; transmitting the encoded two paths of features yuvj and dj to a receiving end through a channel; and at a receiving end, inputting the # imgabs0 # and the # imgabs1 # into a joint source channel decoder, performing feature recovery and up-sampling processing to obtain # imgabs2 # and # imgabs3 #, completing detail recovery in a feature recovery network to obtain # imgabs4 # and # imgabs5 #, and finally reconstructing into an RGBD image. According to the method, the data compression capability of the RGBD image can be improved, and the recovery quality of the image is improved under a relatively low signal-to-noise ratio.

The application discloses a kind of display equipment skin color restoration perceived color difference evaluation method and system, including obtaining reference image and to be evaluated image;Obtain preset reference white point coordinate;Reference image and to be evaluated image are converted from RGB color space to YCbCr color space, and skin color region pixel is extracted according to skin color threshold value;Skin color region RGB value is converted into CIE XYZ value and CIELAB value;The difference between the brightness of two images skin color region, chroma difference, hue difference, average brightness and average hue;Based on the fitting relationship of visual color difference and the average brightness, average hue of image pair of subjective visual evaluation experimental data, determine brightness direction weight parameter, chroma direction weight parameter and hue direction weight parameter, construct skin color difference evaluation model;Color difference parameter and weight coefficient are input into model, to obtain evaluation perceived color difference value, and output skin color restoration quality evaluation result.The application can be used for display equipment skin color restoration evaluation, white balance effect evaluation and related image processing scene.

The invention discloses a document image watermark adding and tampering detection method based on Chebyshev and Arnold. The watermark adding method comprises the following steps: converting a digital paper file into an image file; performing Arnold transformation on the image to obtain a scrambled image xi; converting the image xi into a Ycbcr color space, and extracting a Y channel; two-dimensional Chebyshev transformation is carried out on the Y channel; keeping coefficients greater than a set threshold value mu, and setting the rest coefficients to be zero; performing Chebyshev inverse transformation on the processed Y channel, replacing the processed Y channel into a Y channel in a Ycbcr space, converting the Y channel back to an image, and performing Arnold inverse transformation; and performing Chebyshev inverse transformation on the part of which the coefficient is smaller than a set threshold value mu, and forming encrypted global summary information by extracting noise level features. According to the method, the image storage overhead is hardly increased after the watermark is embedded, and the storage smaller than the original file in size can be obtained under partial conditions, so that the storage and transmission cost is effectively reduced.

The invention provides an image defogging method based on a feature and chromaticity collaborative guide diffusion model, and relates to the technical field of image processing and application, and the method comprises the steps: obtaining a to-be-defogged foggy image; converting the foggy image from an RGB color space to a YCbCr color space to obtain a brightness component and a chrominance component; performing phase consistency extraction on the brightness component to obtain a phase consistency feature; performing chromaticity correction on the chromaticity component to obtain a chromaticity correction feature, fusing the brightness component, the phase consistency feature and the chromaticity correction feature to serve as input of a conditional diffusion model to perform iterative operation, and repairing the brightness component seriously influenced by haze degradation until a noise loss function of the conditional diffusion model meets a preset precision requirement; and outputting the restored brightness component, fusing the restored brightness component with the chromaticity correction feature, and converting the fused brightness component into an RGB space for restoration to obtain a restored defogged image. According to the scheme, the defogging effect of the foggy image can be improved, and high-quality recovery of the foggy image is realized.

The present invention discloses an image hashing authentication method based on texture and statistical features. The image hashing authentication method includes: Step S1: Preprocessing. First, convert the RGB image to the YCbCr color space and perform NSCT transformation on the Y component. Step S2: Feature extraction and representation. Extract texture features and statistical features from the NSCT sub-band images to obtain more useful image information. Among them, the texture features are obtained by mapping the NSCT low-frequency sub-band images using the uniform local binary pattern descriptor (ULBP) into histograms, and the dimension of the vector of this feature is smaller than that of the original local binary pattern descriptor LBP histogram. On the other hand, perform singular value decomposition (SVD) on the NSCT high-frequency sub-band images, and use the singular vector distance to obtain statistical features. Step S3: Combine the above two kinds of texture features and statistical features to generate an image hash and perform image authentication. The present invention has the advantages of simple principle, wide application range, good robustness and discrimination in detection performance, etc.

The present invention discloses a method for target segmentation completion and recognition in an occluded environment, including: collecting two consecutive video sequence images in an occluded environment, converting the images from the RGB color space to the YCbCr color space, extracting the Y component and performing normalization processing; performing mean clustering segmentation to obtain two segmentation images in the occluded environment; performing edge extraction and edge tracking, implementing contour reconstruction of the picking target through an improved interpolation algorithm, and then calculating and generating a fitting rectangle according to the segmentation images with contour reconstruction in the two occluded environments; calculating the relative accuracy, identifying the occluded repair image, and obtaining the segmentation completion and recognition image of the picking target in the occluded environment. The present invention provides an effective method for target segmentation completion and recognition in an occluded environment, and innovatively uses an improved interpolation algorithm to complete the contour of the picking target, having the advantages of strong practicability, high contour completion accuracy, and strong anti-background environment interference ability.

The application discloses a PET and MRI image fusion method based on a three-discriminator generative adversarial network, which comprises the following steps: firstly, performing a transformation operation on a PET image from an RGB color space to a YCbCr color space to obtain three component images of the PET image, namely Y, Cb and Cr; secondly, performing a difference operation on the Y component of the PET image and an MRI image, and then performing a connection operation on the obtained difference image and the Y component of the PET image and the MRI image to input the difference image into a generator network; thirdly, inputting an image generated by the generator and an actual image into three discriminators respectively, and continuously improving the generation ability of the generator and the discrimination ability of the three discriminators through a loop adversarial, so as to finally reach a balanced state of the two types of networks, thereby enabling the generator to comprehensively input image information and generate a fusion image containing both spatial structure information of an original MRI image and functional information of an original PET image.

A video compression method may include providing and transmitting encoder commands for a sequence of one or more pixels in a video frame in a YCbCr color space from a set of layered encoder commands. A specific encoder command for a specific sequence may include one of a copy command, an increment command, or a generate pixel command. The copy command may indicate that the specific sequence is the same as one of a previous pixel in the video frame, a pixel located above the first pixel of the specific sequence in the video frame, or a pixel at the position of the first pixel in the previous video frame. The increment command may indicate that the specific sequence includes a single pixel having a color value equal to the previous pixel in the video frame combined with a signed color increment. The generate pixel command may provide color values ​​for the specific sequence.

The present invention provides a neural network-based JPEG image recompression and decompression model and method. The model includes a data preprocessing module, a convolutional network and spatial pyramid module, and a variational reasoning and arithmetic coding module. The data preprocessing module is used to preprocess an input JPEG image in an RGB color space to generate a JPEG frequency domain image in a YCbCr color space and a 4:2:0 format. The convolutional network and spatial pyramid module is used to process the JPEG frequency domain image in a YCbCr color space and a 4:2:0 format, outputting a fixed-length vector. The variational reasoning and arithmetic coding module is used to process the fixed-length vector to generate a probability distribution of latent variables, and arithmetic code the sampled latent variables to complete the JPEG image recompression. The present invention can reduce the transmission and storage costs of JPEG images.

The invention discloses a reflection removal method, device and equipment for an ultra-high-definition image, a medium and a product, and relates to the technical field of image processing, and the method comprises the steps: converting an input ultra-high-definition image in an RGB format into a Lab color space, a YCbCr color space and an HLS color space; carrying out cross validation on an initial skin area determined based on the YCbCr color space and a YCbCr initial correction result and an HLS color space to obtain a target skin color area, a YCbCr target correction result and an HLS correction result; correcting the ultra-high-definition image and the green reflection pollution points determined in the Lab color space to obtain a Lab correction result; and combining the first correction image, the second correction image and the third correction image which are converted from the Lab correction result, the YCbCr target correction result and the HLS correction result to obtain a reflection-removed image, thereby improving the reflection removal effect of the ultra-high-definition image.

The invention provides a deep counterfeiting identification method and device and electronic equipment, and relates to the technical field of image processing. The method comprises the following steps: acquiring video data to be recognized, and performing face screenshot on the video data to obtain a target face image; the target face image is converted to a YCbCr color space, and YCbCr features are obtained; fusing the RGB feature and the YCbCr feature of the target face image to obtain a fused feature; and inputting the fused features into a counterfeit identification model, extracting local features and global features by the counterfeit identification model, and obtaining an identification result output by the counterfeit identification model according to the local features and the global features. According to the scheme, the original image is converted into the YCbCr color space, thereby facilitating the amplification of a counterfeit clue, fusing the RGB color features with the YCbCr color features, and improving the detection precision. Meanwhile, in consideration of artifacts generated after the video is subjected to high compression, global features are concerned while local features are considered, and the recognition precision of the network is improved.

The invention provides an underwater image quality evaluation model based on multi-feature fusion, and belongs to the technical field of underwater image quality evaluation. The method is mainly used for solving the problem that after an underwater image is enhanced, the quality of the enhanced image cannot be objectively and accurately evaluated, so that the score is inconsistent with the human vision, and aiming at the problems that the underwater image is often blurred and the contrast ratio is low due to light absorption, scattering and color distortion, based on a brightness channel and a chromaticity channel in a YcbCr color space, the score is not consistent with the human vision. The method comprises the following steps: extracting brightness features, chrominance features and saliency features of an underwater image to carry out multi-feature fusion, carrying out model training on a data set by adopting a support vector machine sorting SVMrank model based on machine learning and utilizing the fused multi-dimensional features, and finally carrying out image quality evaluation on a to-be-detected image according to the trained model. And obtaining a quality evaluation score of a unified view angle.

A system for authenticating a subject identification document includes a memory section storing computer-executable instructions and a memory section storing an enrolled document template, ROI information, and a codebook representing partitions of a [Cb, Cr] color plane. The system includes a processor in communication with the memory. The processor executes the instructions that, when executed, cause the processor to receive an image of the document, the image depicting, in a RGB color space, the document. The processor transforms the image of the document to a second image of the document in a YCbCr color space. The processor assigns pixels within a ROI in the second image corresponding to the ROI information to the partitions of the codebook. The processor generates a template based on respective counts of the pixels assigned to the partitions of the codebook. The processor compares the template to the enrolled document template to authenticate document.

The invention provides a bar code image copying detection method. The bar code image copying detection method comprises the following steps: converting an image to be detected into a YCbCr color space; calculating a corresponding cross-channel correlation chromaticity diagram of the image in the YCbCr color space to capture correlation between channels; calculating local extremum mode color texture features of the image in a YCbCr color space and each channel of the introduced cross-channel related chromaticity diagram to capture detail information in a local region of the image; calculating pooling versions of the image in a YCbCr color space and a chromaticity diagram of the image to obtain semi-global color texture features; combining the normalized histograms of the local and semi-global LEP color texture features of the YCbCr color space and the cross-channel correlation chromaticity diagram as final discrimination features; extracting features of the original image and the copied image, inputting the features into a support vector machine, and training a classifier; and for any to-be-detected image, after extracting the features, inputting the features into the classifier obtained by training, and determining whether the to-be-detected image is a copied image according to a feedback result of the classifier.

This invention discloses a multi-illumination-angle spacecraft image fusion method based on unsupervised deep learning. The method comprises the following steps: obtaining a dataset of spacecraft images under multiple illumination angles; converting two different images to the YCbCr color space and performing weighted fusion of the Cb and Cr channels; constructing an encoder-decoder convolutional neural network, training it on a natural image dataset to obtain an optimal model, inputting the Y channels of the two images, extracting features through the encoder and fusing the input features based on the activity levels of the feature maps, and reconstructing the new Y channel through the decoder; and recombining the fused image with the Cb and Cr channels, which is then converted back to BGR space. The invention trains the encoder-decoder on a large natural image dataset to avoid overfitting caused by direct training on a multi-illumination-angle spacecraft image dataset. The method calculates fusion weights based on the activity levels of the feature maps to better fuse the complementary information in spacecraft images from different illumination angles.

This application discloses a method, apparatus, device, medium, and product for removing reflections from ultra-high-definition images, relating to the field of image processing technology. The method includes converting an input ultra-high-definition image in RGB format into Lab color space, YCbCr color space, and HLS color space; cross-validating an initial skin region determined based on the YCbCr color space and the initial YCbCr correction result with the HLS color space to obtain a target skin region, a YCbCr target correction result, and an HLS correction result; correcting green reflective pollution points determined by the ultra-high-definition image and the Lab color space to obtain a Lab correction result; and merging a first corrected image, a second corrected image, and a third corrected image converted from the Lab correction result, the YCbCr target correction result, and the HLS correction result to obtain a reflection-removed image. This application improves the reflection removal effect of ultra-high-definition images.

The invention discloses a weak light image enhancement method based on YCbCr brightness guidance, and the method specifically comprises the steps: converting a normal light image and a weak light image from an RGB color space to a YCbCr color space, obtaining a brightness component, a blue component and a red component, inputting all the brightness components into a brightness component diffusion enhancement branch, obtaining an enhanced component, enabling the enhanced brightness component to be subjected to the YCbCr brightness guidance, and enabling the enhanced brightness component to be subjected to the YCbCr brightness guidance to be subjected to the YCbCr brightness guidance. The blue component, the red component and the enhanced component of the weak light image are spliced and then input into a chrominance component recovery branch to obtain an enhanced blue component and an enhanced red component, the enhanced brightness component, the enhanced blue component and the enhanced red component are converted into an RGB color space to obtain an enhanced image, and loss constraint is carried out to obtain an enhanced image; and a final enhanced image is obtained. According to the method, the problems of poor color and brightness recovery effect and unclear enhancement result texture of the existing method are solved.

Provided is an image processing method for visualizing fine irregularities in a lower layer portion covered by blood. An original image is converted into an RGB color space, filter monochromatization is performed on each pixel of the image converted into the RGB color space, a gray plane is generated, and when the gray plane is generated, a color emphasis filter is used, and the luminance ratio (Y1) of only a region having a specific hue is increased. Further, the original image is converted into a YUV color space, a YCbCr color space, or a YPbPr color space, the converted image and the gray plane are compared for each pixel by luminance (Y), the higher (bright) one is used as a color image, and the color image is sharpened.

The present invention proposes a document image shadow removal method based on attention-guided detail restoration, comprising the following steps: step S1, using YCbCr color space transformation and brightness information to generate a shadow attention map as a label to identify the shadow area; step S2, through the shadow attention generation sub-network, using a multi-scale large-core attention mechanism, extracting shadow features, and obtaining an optimal attention map; step S3, dynamically fusing the optimal attention map and shadow features; step S4, through the detail refinement sub-network, using lightweight spatial channel convolution to calculate and restore image details; step S5, based on the loss function, optimizing network parameters through end-to-end training; step S6, using the trained network to perform shadow removal processing on the document image. The method aims to effectively remove complex shadows in document images while retaining image details, and to achieve this with low computational complexity.