10168results about "Image coding" patented technology

The invention is suitable for the technical field of video acquisition, and particularly relates to a video stream acquisition method based on deep learning, and the method comprises the steps: carrying out the video acquisition through a self-adaptive strategy, and obtaining real-time collected video data; performing parameter adjustment through edge calculation to obtain preprocessed video data; performing key frame selection on the preprocessed video data to obtain key frame data; performing inter-frame difference compression on the key frame data through the optimized variational auto-encoder model, and outputting video frame compression data; and when the original video needs to be recovered, analyzing the video compression data, reconstructing through the pre-trained neural network model to obtain a reconstructed video sequence, and generating a complete video according to the reconstructed video sequence. According to the method, the non-key frame is directly generated in the decoding process by using the deep learning model, so that the compression ratio can be remarkably improved, and the video playing continuity and the image quality, especially the performance in high-dynamic and complex scenes, can be improved.

Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. A novel correlation network composed of convolutional layers for feature extraction that extract multi-dimensional features from the image and a channel-wise transformer with attention to capture complex inter-channel dependencies. An angle optimizer enhances compressibility of an image and an error resilience subsystem improves robustness against transmission errors and data loss. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission. The correlation network incorporates error correction and concealment techniques during decoding. The model's outputs enable effective image reconstruction, achieving advanced compression while preserving information for accurate analysis.

The invention provides a cross-modal image-text retrieval processing method and a cross-modal image-text retrieval processing system, which are applied to the field of information retrieval, and the method comprises the following steps: obtaining a query text input by a user in an image-text retrieval process; encoding the query text through a text encoder to generate a query text feature vector; through a cross-modal image-text retrieval model, similarity matching is carried out based on query text feature vectors and multi-modal embedding representations stored in an external knowledge base, related results corresponding to the multi-modal embedding representations larger than a matching threshold value are returned, and the multi-modal embedding representations are used for representing joint features of images and texts; when the related result comprises the image and the text at the same time, the related result and the query text are input into a preset multi-modal large model, image question answering with text assistance is carried out, and a retrieval result output by the multi-modal large model is obtained; according to the method, the semantic association between the image and the text can be better captured, so that the accuracy of image-text retrieval is improved.

Systems and methods for image compositing are provided. An aspect of the systems and methods includes obtaining a first image and a second image, wherein the first image includes a target location and the second image includes a target element; encoding the second image using an image encoder to obtain an image embedding; generating a descriptive embedding based on the image embedding using an adapter network; and generating a composite image based on the descriptive embedding and the first image using an image generation model, wherein the composite image depicts the target element from the second image at the target location of the first image.

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

Embodiments described herein provide a video generation framework built on a decoupled multimodal cross-attention module to simultaneously condition the generation on both an input image and a text input. The video generation may thus be conditioned on the visual appearance of a target object reflected in the input image. In this way, zero-shot video generation may be achieved with little fine-tuning efforts.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

The present disclosure provides a variable-bit-rate image compression method and system, an apparatus, a terminal, and a storage medium. The variable-bit-rate image compression method includes: obtaining an initial feature map from a to-be-encoded image; quantizing the initial feature map by a dead-zone quantizer; performing entropy encoding on the quantized feature map and hyper-prior information to obtain a compressed bit-stream; performing entropy decoding on the compressed bit-stream, and recovering quantized hyper-prior information and the quantized feature map; performing inverse quantization on the quantized feature map to obtain a reconstructed feature map; obtaining a reconstructed image from the reconstructed feature map; and adjusting quantization and inverse quantization parameters according to a target bit-rate or target distortion. The present disclosure provides a precise bit-rate control solution, makes the bit-rate of the compressed bit-stream better adapt to the dynamic change of a network bandwidth, and has an extremely high actual application value.

A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.

A mesh and point cloud coding method and an apparatus predict with reference to a reconstructed point cloud in encoding / decoding a mesh to increase coding efficiency for three-dimensional meshes and point clouds. Alternatively, the mesh and point cloud coding method and the apparatus predict with reference to a reconstructed mesh in encoding / decoding a point cloud.

The invention belongs to the technical field of tree three-dimensional reconstruction, and particularly discloses a tree neural rendering and reconstruction method based on a sparse view, and the method comprises the steps: collecting an RGB image of a tree through a camera, obtaining camera parameters, inputting the camera parameters into a pre-trained depth estimation model, generating a rough depth map, and inputting the rough depth map into a neural radiation field network; introducing a cone sampling strategy, integrating a position coding method, a coding annealing strategy, depth sorting and depth consistency constraint, and extracting point cloud and picture features in the picture; carrying out implicit surface reconstruction based on volume rendering; introducing color constraint and geometric constraint based on color deviation and geometric deviation of implicit surface reconstruction; and a three-dimensional model of the tree is reconstructed from the implicit surface by using a Marking Cubes algorithm. By adopting the technical scheme, tree rendering is carried out based on the neural radiation field, tree reconstruction is carried out based on body rendering, and the reconstructed tree model can better retain and reproduce original geometric features and real textures of the tree.

A method for lossy image and video encoding, transmission and decoding, the method comprising the steps of: receiving an input image at a first computer system; encoding the input image using a first trained neural network to produce a latent representation; performing a quantization process on the latent representation to produce a quantized latent; transmitting the quantized latent to a second computer system; decoding the quantized latent using a denoising process to produce an output image, wherein the output image is an approximation of the input image.

The invention relates to a method and a system for inhibiting strong mutual reflection based on polarization complementary logic coding. The method comprises the following steps: acquiring calibration parameters of a camera and a projector; generating a polarization phase shift stripe based on the calibration parameter, designing a high-frequency complementary logic code word, and embedding the high-frequency complementary logic code word into a polarization channel through polarization modulation to obtain a composite stripe pattern; the method comprises the following steps: calculating to obtain a linear polarization degree image through an acquired multi-angle polarization image, extracting a high-reflection region from the linear polarization degree image by adopting an adaptive threshold segmentation method, and carrying out binarization processing on the linear polarization degree image to obtain a binarized linear polarization degree mask; and on the basis of a decoding range of a binary linear polarization degree mask constraint high-reflection region, decoding the composite stripe pattern to obtain absolute phase level information, and performing noise suppression on the absolute phase level information by adopting a self-adaptive threshold algorithm. The method can effectively solve the influence of mutual reflection and high dynamic range of the surfaces of the measured object, and improves the precision and stability of three-dimensional reconstruction.

A computer-implemented method for decoding a coded mesh bitstream of a dynamic mesh representing three-dimensional content includes that: geometry information of the dynamic mesh is reconstructed from a geometry component bitstream in the coded mesh bitstream, the reconstructed geometry information includes data specifying vertices of the dynamic mesh; connectivity information of the dynamic mesh is reconstructed from a connectivity component bitstream in the coded mesh bitstream, the reconstructed connectivity information includes data specifying faces of the dynamic mesh; the reconstructed connectivity information is refined based on the reconstructed geometry information to generate refined connectivity information by at least dividing a face specified by the reconstructed connectivity information into two faces based on a vertex specified in the reconstructed geometry information; the dynamic mesh is reconstructed based on the reconstructed geometry information and the refined connectivity information; and the reconstructed dynamic mesh is caused to be rendered for display.

An apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: determine information related to a usability of at least one image with an artificial intelligence model; and signal the information related to the usability of the at least one image with an artificial intelligence model.

The invention discloses a multi-scale semantic guidance image compression method and system and a storage medium, and the method comprises the following steps: obtaining input image data, carrying out the preprocessing of an input image, and obtaining standardized image data; inputting the standardized image data into a pre-trained semantic segmentation network to generate a multi-scale semantic feature map and a semantic weight map corresponding to the multi-scale semantic feature map; a three-stage pyramid encoder is constructed, and the standardized image data is subjected to the following steps of: sampling under depth separable convolution to generate multi-scale features; the reversible neural network carries out nonlinear transformation on the multi-scale features; the multi-scale feature subjected to nonlinear transformation is decomposed into a low-frequency sub-band and a high-frequency sub-band through adaptive discrete wavelet transformation, dynamic selective state space modeling is executed on the high-frequency sub-band based on a semantic weight map, and a compressed code stream is generated; and inputting the compressed code stream into a decoder, decoding based on a lightweight Mama module, and reconstructing an image in combination with inverse wavelet transform and a semantic weight map.

The invention discloses a bad weather image restoration method based on a multi-modal state space model, and belongs to the technical field of computer vision. In order to solve the problem that an existing unified bad weather image restoration method has limitations in the aspects of global receptive field and computational efficiency, the unified restoration of various bad weather images is realized by designing a parallel multi-mode encoder and an adapter to generate comprehensive prompts containing degeneration semantics. Through parallel connection of a state space module and a local context sensing module of a double-attention mechanism, simultaneous capture of global long-range dependence and local detailed features is realized. Experiments show that the method is high in generalization ability, high in processing speed and low in calculation complexity, and can show good adaptive capacity on a plurality of bad weather image removal tasks.

The disclosure relates to a data processing method and a related device for point cloud media. The method includes: obtaining a media file of point cloud media, the media file comprising a point cloud bitstream and cross-attribute dependency indication information of the point cloud media, and the cross-attribute dependency indication information being for indicating an encoding and decoding dependency relationship between attribute data in the point cloud bitstream; and decoding the point cloud bitstream based on the cross-attribute dependency indication information to present the point cloud media.

The invention discloses a point cloud data processing method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring initial point cloud data to be sent; dividing the initial point cloud data into a group of point cloud blocks; performing graphical representation on the basis of the point cloud data in each point cloud block in the group of point cloud blocks to generate a group of graph structure data, one graph structure data in the group of graph structure data corresponding to one point cloud block in the group of point cloud blocks, and the graph structure data being used for representing features of the corresponding point cloud block; and encoding the group of graph structure data to obtain a group of data packets, and sending the group of data packets to a target terminal so as to perform decoding reconstruction at the target terminal to obtain target point cloud data. The technical problems that the processing efficiency of the point cloud data is low and the effect cannot be quickly achieved in the related technology are solved.

A method for processing media data is disclosed. In an embodiment, the method includes determining a value of a neural-network post-filter characteristics (NNPFC) purpose (nnpfc_purpose) in a NNPFC supplemental enhancement information (SEI) message, wherein the nnpfc_purpose is configured to be set to include output picture size downsampling, and wherein at least one of (1) an output picture width is not equal to an input picture width and (2) an output picture height is not equal to an input picture height; and performing a conversion between a visual media data and a bitstream based on the nnpfc_purpose.

The invention relates to the technical field of computer vision, and discloses a thyroid ultrasonic robot automatic scanning method, device and equipment based on RGB images and depth information and a medium, and the method comprises the steps: obtaining image information and depth information, coding the depth information, fusing and recognizing a target scanning area, determining an initial scanning point and an initial scanning direction, controlling the scanning probe to scan and collect a real-time scanning image, analyzing the real-time scanning image to recognize a preset target and an artifact area, adjusting a scanning posture and a scanning path based on a recognition result, monitoring a continuous existence state of the preset target, and stopping scanning when the preset target is not recognized continuously. The target area is identified by fusing the multi-modal image information, the scanning posture and path are dynamically adjusted in combination with real-time image analysis, scanning termination is intelligently controlled according to the target detection result, the positioning accuracy, image quality and standardization level of ultrasonic scanning are improved, and the method is suitable for automatic ultrasonic imaging of thyroid and superficial organs.

Ways to post-process a decoded mesh and modify received triangles per patch to improve the mesh geometry are described herein. Since the transmitted geometry contains the high-resolution surface information, the information is able to be applied to the mesh reconstruction operation to generate triangles that are aligned with the original surface. Methods include generating new triangles by splitting the received triangles' edges according to their size, by inserting new vertices at the triangle's centroids, by splitting the vertices, and by performing marching cubes in surfaces defined by the geometry images.

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data.

Techniques for latent space based steganographic image generation are described. A processing device, for instance, receives a digital image and a secret that includes a bit string. A pretrained encoder of an autoencoder generates an embedding of the digital image that includes latent code. A secret encoder is trained and utilized to generate an embedding of the secret to act as a latent offset to the latent code. The processing device leverages a pretrained decoder of the autoencoder to generate a steganographic image based on the embedding of the secret and the embedding of the digital image. The steganographic image includes the secret and is visually indiscernible from the digital image. Further, the processing device is configured to recover the secret from the steganographic image, such as by training and leveraging a secret decoder to extract the secret.

The embodiment of the invention provides a dynamic point cloud compression method and device based on time sequence perception, and the method comprises the steps: carrying out the sparse convolution processing of two continuous frames of input point cloud data, and obtaining point cloud features in a feature space, carrying out local and global feature extraction on representations of two continuous frames in the feature space, splicing and fusing to obtain a motion vector, and carrying out motion compensation on point cloud features of the previous frame by utilizing the accurate motion vector to obtain a prediction frame of the current frame; calculating a residual error between a prediction frame and an actual frame, carrying out compression coding to obtain a residual error tensor, adding the prediction frame and the residual error tensor to obtain a coarse-grained prediction feature, and carrying out time alignment and feature fusion on the coarse-grained prediction feature and the three-frame point cloud features stored in the buffer region to obtain a fused prediction feature; performing up-sampling reconstruction on the fused prediction features to obtain a final point cloud frame; according to the invention, the accuracy and efficiency of point cloud data compression can be effectively improved.

A method discloses receiving a plurality of input images, receiving text prompts, generating a visual matrix utilizing the images and an image encoder, generating a text matrix utilizing a text encoder, multiplying the text matrix and the visual matrix to generate an image-text similarity matrix that assigns a numerical value indicating similarities between each of encoded visual descriptors and each of the encoded images, wherein similarities are indicated by entries of the image-text similarity matrix having numerical values that determine a loss function associated with the image-text similarity matrix, identify a gradient of the loss function with respect to parameters associated with the image encoder and parameters associated with the text encoder, utilizing the gradient, update parameters associated with the image encoder or the text encoder, and outputting final updated parameters associated with either the text encoder or image encoder of the machine learning network.

A method for learning-based point cloud geometry compression includes: given a source point cloud, regressing an aligned mesh that is driven by a set of parameters from a deformable template mesh, quantizing the set of parameters into a parameter bitstream, generating an aligned point cloud from the quantized parameters by mesh manipulation and mesh-to-point-cloud conversion, extracting features from both the source point cloud and the aligned point cloud based on sparse tensors including coordinates and features, the coordinates being encoded into a coordinate bitstream, warping the features of the aligned point cloud onto the coordinates of the source point cloud, obtaining residual features through feature subtraction, processing the residual features using an entropy model into a residual feature bitstream, and obtaining a reconstructed point cloud by processing the parameter bitstream, the coordinate bitstream and the residual feature bitstream.

The invention provides a processing method and system for reconstructing an LDI sub-pixel image through sparse compression and application, and the method comprises the steps: calculating a low-resolution to-be-exposed image corresponding to each phase structure in advance through a computer image compression algorithm; the low-resolution to-be-exposed images obtained through calculation are loaded to the digital micro-reflector according to the time sequence, and the display moment of each pattern is matched with the rotation position of the corresponding phase structure; coupling the pattern of the digital micro-mirror and the phase encoding mask in a frequency domain through a 4-f optical system, and reconstructing a high-resolution exposure pattern at a sub-pixel level by utilizing a diffraction effect; projecting a high-resolution light field of the reconstructed high-resolution exposure pattern to the surface of the photoresist, and forming a target circuit pattern by accumulating exposure dose; and the micro-nano structure with sub-pixel precision is obtained after development. The system comprises an image compression module, a pattern matching module and a pattern forming module. According to the invention, the manufacturing cost of the laser direct imaging equipment is reduced under the condition of the same precision.

There is provided methods and apparatus of encoding / decoding a point cloud representing a physical object. Points are captured by a spinning sensors head and are represented by sensor indices associated with sensors that captured the points, azimuthal angles representing capture angles of said sensors, and radius values of spherical coordinates of the point. Points are ordered based order indices obtained from the azimuthal angles and the sensor indices. Order index differences are encoded. An order index difference represents a difference between order indices associated with two consecutive ordered points. Optionally, the method encodes radius values, residual azimuthal angles associated with ordered points and residuals of three-dimensional cartesian coordinates of ordered points based on their three-dimensional cartesian coordinates, decoded azimuthal angles based on azimuthal angles, decoded radius values and sensor indices.

The invention provides a decoupling type image style migration method based on a diffusion model, and relates to the field of image processing, and the method comprises the steps: constructing an image style migration model based on content and style decoupling, the image style migration model at least comprises a content extraction module, a style extraction module, a projection network and a style migration module, and the model decouples contents and styles from a feature level and a network level at the same time; constructing a training set covering a plurality of data types; training the image style migration model through the training set and the loss function; based on the trained image style migration model, the content image and the style image are input, the stylized result image with the target content and the target style is generated, and the method has the advantages that the style migration effect is improved, and more interpretability is achieved.