65 results about "Image complexity" patented technology

The invention discloses an artificial intelligence machine vision image acquisition system, and the system comprises a multi-mode perception layer which integrates a self-adaptive optical module, inhibits metal reflection, captures a visible light to short wave infrared image, and captures a motion edge; the dynamic adaptive layer adopts an illumination compensation and motion compensation module to dynamically adjust camera parameters and micro displacement compensation, feeds back an illumination trend, outputs a motion vector to the cognitive layer, generates a confrontation sample through a GAN, simulates virtual defects in combination with a physical engine, and expands training data; the cognitive reasoning layer is used for deploying a dynamic routing network, distributing computing resources according to image complexity and optimizing feature extraction efficiency; reducing data deviation through anti-fact analysis, and generating a thermodynamic diagram to explain a detection basis; and the collaborative decision-making layer is used for rapidly screening samples by edge nodes, training a global model by cloud aggregated data, automatically triggering manual rechecking when the confidence coefficient of the model is insufficient, synchronously optimizing a training set and a causal reasoning module by a rechecking result, and improving the labeling efficiency through AR assistance.

A system and method are disclosed for low-light image enhancement using hierarchical adaptive wavelet decomposition with cross-scale feature fusion. The system analyzes a raw input image to determine image characteristics and preprocessing parameters. A hierarchical adaptive wavelet decomposition process creates a variable-depth decomposition tree comprising frequency domain nodes, with decomposition depth determined by local image complexity. Cross-scale feature fusion implements attention mechanisms between nodes at different decomposition levels, enabling bidirectional information flow across scales. A dynamic network pool allocates specialized neural networks to process nodes based on their frequency characteristics, with weight sharing between similar nodes for efficiency. An adaptive reconstruction engine traverses the decomposition tree using learned filters and multi-scale residual learning to produce an enhanced image. The hierarchical approach enables superior low-light image enhancement by allocating computational resources based on content complexity, achieving better quality than fixed decomposition methods while maintaining compatibility with existing image signal processing pipelines.

The invention relates to a method for dynamically adjusting parameters of a video encoder, which comprises the following steps of: acquiring a visual characteristic index corresponding to a video frame image in real time, and calculating an image complexity score of the image according to the visual characteristic index; the visual feature indexes comprise motion intensity, edge density and information entropy; acquiring a network index of a video transmission network in real time; the network indexes comprise available bandwidth, delay, jitter and packet loss rate; obtaining a current encoder control parameter according to the current image complexity score and the network index; the control parameters of the encoder comprise GOP, GP, FPS and Buffer Size; and adjusting the encoder according to the obtained control parameters of the encoder. The method is superior to an existing fixed parameter coding system in the aspects of image quality, delay control, system robustness and adaptability.

The invention relates to the technical field of artificial intelligence image processing and biological detection crossing, and discloses a perception enhancement and feature distillation-oriented high-density microalgae detection method, which comprises the following steps: acquiring a high-density microalgae image containing cell overlapping, boundary blur and cross-scale distribution, and inputting the image into a backbone network to extract a multi-scale feature map; optimizing the bounding box through potential consistency mapping processing; generating a microalgae density map through density sensing auxiliary processing, and calculating density loss; self-adaptive denoising is carried out based on image complexity; a student model is optimized by using a dual-feature distillation framework, and the small-scale microalgae detection capability is enhanced; and finally, fusing the results of the modules, and outputting the position, category and quantity of the microalgae. According to the method, the overall average accuracy of high-density microalgae detection can be improved, bounding box jitter and small-scale microalgae omission ratio are reduced, the average reasoning time is shortened, the real-time detection requirement is met, and reliable data support is provided for microalgae culture process control and optimization.

The invention discloses an efficient and intelligent road disease data marking method based on large model cooperation, and particularly relates to the technical field of road detection. Various core features are extracted from two dimensions of disease ontology and identifiability, the complexity of the disease and the complexity of the identification difficulty are comprehensively covered, the limitation of a single feature is avoided, the two types of complexity are quantified through a disease evaluation coefficient and a disturbance evaluation coefficient respectively, then the complexity is fused into a complex evaluation coefficient, and the complexity judgment is completed by grading according to a coefficient interval. The problems that in the prior art, a fixed model full-amount calling mode is mostly adopted, judgment on image complexity mostly depends on a single feature, and the cooperative influence of a disease ontology feature and an environment interference feature is ignored are solved.

The invention discloses a self-adaptive image generation method and system based on multi-modal routing, and the method comprises the steps: extracting the multi-scale features of an input image, and generating three different tokens with progressive increase of continuous information keeping capability: respectively extracting visual and text modal information from the image and corresponding text description, and generating a multi-modal information abstract after fusion; inputting the multi-modal information abstract into a learnable soft router, and dynamically selecting a token modeling path based on an image complexity label; a three-stage training strategy optimization model is adopted; in the inference stage, a trained soft router dynamically selects a token path to complete image generation according to input text description and an image information abstract predicted by an autoregressive Transform. According to the method, a dynamic router and three quantification and modeling strategies with different complexities are fused, and dynamic modeling path selection is realized in a reasoning stage through a soft router module. According to the method, on the premise of ensuring the generation quality, the reasoning efficiency is effectively improved, and good efficiency is shown.

The invention relates to a cloud particle data target detection method based on image complexity. The method comprises the following steps: firstly, calculating weighted information entropy, weighted texture complexity and weighted local standard deviation of data of each cloud particle image, and evaluating the complexity of the image data; and marking according to the complexity of the image data, further calculating the comprehensive complexity of the data group, and judging the uniformity degree of complexity distribution of the data group. On the basis, different splitting and combining strategies are adopted according to the overall complexity distribution uniformity and marks of the data sets, and it is ensured that the deep learning detection model can select the most suitable strategy according to the complexity. Through the method, the computing resources and the detection precision of the deep learning model can be optimized in the face of cloud particle image data with different complexities, the image processing efficiency is improved, and the method is suitable for large-scale cloud particle data sets with large complexity differences.

The invention discloses an image encryption method based on interpretable block model driving. The method comprises the following steps: inputting a digital image to be processed; calculating the spatial information complexity of the image; calculating the statistical information complexity of the image; calculating the visual perception complexity of the image; establishing a weight optimization objective function; the optimal weight configuration is solved; calculating an image complexity evaluation value, and calculating the image complexity by using the optimal weight configuration; the maximum and minimum block size logarithms are set, and the self-adaptive block size is determined; setting parameters of the Chen's chaotic system; generating a pseudorandom sequence, and starting the Chen's chaotic system by using a set initial value to obtain a three-dimensional chaotic sequence; inputting a plaintext image, extracting basic parameters, image height and image width, and calculating the total number of image pixels; according to the method, a completely quantifiable block decision model is constructed, the mapping process from the image features to the block strategy is accurately described through a mathematical formula, and the problem that a traditional block method lacks a theoretical basis is fundamentally solved.

Disclosed in the embodiments of the present application are a tree-structure-based object rendering method and apparatus, and an electronic device, a computer-readable storage medium and a computer program product. The method comprises: acquiring an object node evaluation list associated with a three-dimensional virtual object to be rendered in a virtual scene; acquiring an image complexity of a target texture map of a target node; on the basis of a target node bounding box to which the target node belongs, determining target size information of the target node bounding box, and on the basis of the target size information, a camera line-of-sight between the target node and a target camera, and the image complexity, performing node evaluation on the target node to obtain a node evaluation result; and if the node evaluation result indicates that the target node meets a rendering display condition and the target node is located within the field-of-view corresponding to the target camera, when the three-dimensional virtual object is rendered and displayed, rendering and displaying the target node, and performing node hiding on a child node of the target node.

The invention provides a medical image intelligent labeling and auditing method based on deep learning, and relates to the technical field of medical image labeling, and the method comprises the steps: carrying out the preprocessing of medical image data, and obtaining the preprocessed image data; performing two-dimensional evaluation on the preprocessed image data based on image complexity and labeling task complexity to obtain a total complexity score; dividing the preprocessed image data into a simple level, a medium level and a complex level according to the total complexity score; carrying out labeling processing on the preprocessed image data by adopting a differential labeling strategy to obtain a labeling result; performing quantitative evaluation on the labeling result to obtain a quality score; extracting labeling process features, and dividing labeling results into high quality, medium quality and low quality; and determining an auditing strategy, auditing the annotation result, and outputting the annotation result which is audited to be qualified. According to the method, objective evaluation of labeling quality and reasonable configuration of auditing resources can be realized, and the consistency of auditing is guaranteed.

The application relates to a deep learning-based image processing method, device, equipment and medium. The method comprises the following steps: acquiring a to-be-processed dynamic video stream, wherein the to-be-processed dynamic video stream comprises to-be-processed images of each video frame; acquiring a hardware resource state parameter corresponding to a video frame of the to-be-processed image, and calculating a corresponding hardware state value of each to-be-processed image based on the hardware resource state parameter; calculating the image complexity and the interframe difference degree of the to-be-processed image; performing image processing deep learning model state judgment on the to-be-processed image based on the hardware state value, the image complexity and the interframe difference degree, and generating an image processing deep learning model state judgment result of the to-be-processed image. The method can realize hardware adaptation and dynamic model architecture fine-tuning, and significantly improve the model inference efficiency, image processing quality and hardware resource utilization rate of video stream processing.

The invention relates to the technical field of information hiding, and discloses a self-adaptive reversible information hiding method and extraction method based on image complexity. The method comprises the following steps: firstly, calculating a global Shannon entropy setting complexity threshold value, and recursively partitioning; discrete wavelet transformation is performed on the high-complexity sub-graph, secret information is embedded into a low-frequency sub-band through prediction error extension, and then inverse transformation is performed and the secret information is spliced with other sub-graphs to generate a secret-carrying graph. During extraction, information can be extracted and an original image can be recovered without loss by using the same threshold value and the same blocking rule and carrying out reverse operation on the secret-containing sub-image. According to the method, the embedding capacity is dynamically adjusted on the premise of ensuring the visual quality, the calculation overhead is low, the safety is high, and the method is suitable for scenes of digital watermarking, copyright protection, medical images, secret communication and the like.

The invention discloses a VGGT model accelerated reasoning method based on dynamic sparse calculation, and the method comprises the following steps: carrying out the normalization preprocessing of an input image sequence, and enabling the input image sequence to meet the input requirements of a VGGT model; and generating a dynamic sparse mask for subsequent sparse calculation according to the input image and the output of the model intermediate layer. And then, in the VGGT model, reasoning is carried out by adopting a sparse calculation method, so that the calculation amount is effectively reduced. According to image complexity and model performance feedback, the sparse rate is dynamically adjusted, and calculation efficiency and model precision are both considered. And finally, outputting final prediction results such as a camera pose code, a depth map and a point tracking result. According to the method, the VGGT model reasoning process is optimized through dynamic sparse calculation, certain precision is guaranteed while the calculation efficiency is improved, an efficient solution is provided for related image analysis and processing tasks, and the method can be widely applied to the field of computer vision such as automatic driving and intelligent monitoring scenes.

The application relates to the field of screen control, in particular to a control system based on intelligent multi-screen cooperation, which comprises a processing unit, an analysis unit, a load optimization unit and a display optimization unit. The processing unit comprises a plurality of sub-processing modules and is used for processing target videos respectively. The analysis unit is used for determining the display state of each target screen according to a picture misadjustment reference value and a display decline reference value, and determining an optimization mode as load distribution optimization or environment optimization according to a first display state proportion and a first display state aggregation degree. The load optimization unit is used for determining the screen category of the target screen according to an image complexity reference value and a dynamic reference value, and determining an optimization processing mode as task distribution optimization or task order optimization based on a balance coefficient. The display optimization unit is used for determining the environmental light influence state corresponding to each target screen according to an illumination intensity coefficient and an illumination distribution coefficient, and determining whether to adjust the brightness of the target screen according to the environmental light influence state. The application improves the display effect under the control of the multi-spliced screen.

This invention discloses a method and system for generating digital watermarked images. The method includes: acquiring an original image to be watermarked; generating an image coding mask based on the image complexity of the original image; performing frequency domain decomposition on the original image to obtain a low-frequency component image; embedding an initial watermark image into the low-frequency component image according to the embedding strength values ​​corresponding to the masks in the image coding mask to obtain a low-frequency watermark image; different masks in the image coding mask correspond to different embedding strength values; and performing an inverse frequency domain transform on the low-frequency watermark image to obtain a synthesized watermark image. This invention can improve the success rate and accuracy of watermark extraction.

The invention discloses a blood vessel recognition method, system and device based on image features and a medium, and relates to the technical field of data transmission processing, and the method comprises the steps: obtaining a target image, and dividing the target image into a plurality of sub-pixel regions; obtaining a gray gradient amplitude of each sub-pixel region, and obtaining a dynamic extension threshold of two adjacent sub-pixel regions; acquiring gray variation of two adjacent sub-pixel areas, and connecting the two adjacent sub-pixel areas with the gray variation smaller than a dynamic extension threshold to form a to-be-extended section; obtaining a preset angle threshold value, obtaining the number of the to-be-extended sections formed by each sub-pixel region, and obtaining a target angle threshold value according to the number of the to-be-extended sections formed by each sub-pixel region and the preset angle threshold value; if the included angle between the adjacent to-be-extended sections is larger than or equal to the target angle threshold value, the adjacent to-be-extended sections are connected, and a to-be-blood-vessel section is formed. The method has the advantages of image complexity perception, bifurcation and extension recognition and adaptive threshold adjustment.

The invention discloses an image anti-counterfeiting method based on vector quantization and a phase mask, and the method comprises the steps: firstly, selecting a two-dimensional code which is simple in structure and only has transverse and longitudinal black and white change characteristics as an information medium, and simplifying a subsequent phase modulation structure while keeping the recognition characteristics clear; a vector quantization image compression technology is utilized to effectively code a high-definition image with rich content into a simplified graphic expression in which a two-dimensional code can be embedded, and effective mapping and compression from image complexity to a barcode structure are realized; and finally, a low-order pure-phase optical mask is generated based on the two-dimensional code, embedding and optical reconstruction of a high-definition image are realized under the condition that the phase quantization order is relatively low, and both the image identification degree and the mask preparation feasibility are considered. Therefore, the innovative image-level anti-counterfeiting coding method which integrates physical anti-counterfeiting and digital anti-counterfeiting advantages and has high information bearing capacity and strong physical anti-counterfeiting performance is constructed. The invention provides a feasible, efficient and low-cost new path for physical anti-counterfeiting of high-definition images.

This application provides a printing timing control method, a printer, a program product, and a storage medium, relating to the field of electronic digital data processing. Utilizing a double-buffered structure of a character buffer and a transmit character buffer, the generation process of print data is physically isolated from the hardware transmission process. This avoids the serial dependency of waiting to transmit and transmitting while waiting to generate, as found in related technologies. It allows the main control module to pre-generate subsequent data and store it in the first-level cache during the hardware transmission intervals. This smooths out fluctuations in data generation time caused by differences in image complexity and avoids hardware stalls caused by computational delays.

This invention relates to the field of vehicle-mounted video coding technology, and discloses a preprocessing method, system, vehicle, and medium for vehicle-mounted video coding bitrate. The method includes: performing multi-dimensional feature detection on the original image frame before coding, and generating a comprehensive complexity factor reflecting the bitrate fluctuation of vehicle-mounted video coding based on the detection results; detecting targets in the original image frame, and identifying core and background regions in the original image frame based on the targets; performing corresponding image preprocessing operations on the core and background regions according to the comprehensive complexity factor to obtain the target image frame. This method does not rely on the underlying modification of the hardware encoder, but only fuses the relevant elements in the vehicle scene that are most likely to cause bitrate fluctuations through multi-dimensional feature detection, so that the intensity of subsequent preprocessing is precisely matched with the image complexity, thereby effectively suppressing bitrate peaks and improving bitrate stability; and performing differentiated processing on different regions, ensuring the effectiveness of the core region while controlling the overall bitrate.

The invention discloses a remote sensing image subtitle generation method and system. The method comprises the steps of obtaining a combined remote sensing image data set; for each remote sensing image in the combined remote sensing image data set, generating first annotation information of the remote sensing image based on a remote sensing image category, a target detection bounding box and a semantic segmentation mask, determining image complexity based on the first annotation information, and determining a subtitle generation mode according to the image complexity, the subtitle generation mode comprises generation of subtitles according to rules, generation of subtitles by a multi-modal large model and generation of subtitles by combining generation of subtitles according to rules and generation of subtitles by the multi-modal large model; generating subtitles of the remote sensing image based on a subtitle generation mode; all the image subtitle pairs form a remote sensing image-text pairing data set; and training a CLIP model based on the remote sensing image-text pairing data set. According to the method, the workload of manual labeling is effectively reduced, and the construction cost of the high-quality remote sensing image-text data set is reduced.

This invention proposes a method and system for Thangka image restoration, relating to the field of image restoration. The method includes: preprocessing the acquired original Thangka images; classifying the preprocessed images into first, second, third, and fourth Thangka images according to their complexity from low to high; sequentially performing damage processing on these Thangka images to form structurally damaged, textured, semantically abnormal, and mixed-damaged images; training these damaged images to form a Thangka restoration model; and using the Thangka restoration model to restore the Thangka image to be restored, forming the target restored image. This invention combines the classification and damage processing of different Thangka image complexities to obtain different types of damaged images, adapting to different damage scenarios. Furthermore, by training and optimizing different damaged images separately, the resulting Thangka restoration model can comprehensively restore Thangka images, ensuring that the restored image has a regular structure, natural texture, reasonable semantics, and high fidelity.

The invention discloses a method and a device for detecting distribution uniformity of laser integral light intensity, and belongs to the technical field of laser. According to the method, the laser integral light intensity distribution images at different sampling moments are obtained, the laser integral light intensity distribution images are cut, effective image areas are identified, laser transmission distortion and edge noise interference are avoided, the image complexity is reduced, resources needed by subsequent detection are reduced, and the detection efficiency is improved. Therefore, the detection efficiency is improved. By drawing a plurality of reference lines and acquiring the light spot intensity data and the light spot intensity fitting curve of each reference line, the dimension of a surface can be represented by the dimension of a line, the data processing amount is reduced, and the detection efficiency is improved; the laser integral light intensity distribution characteristic value is determined through the light spot intensity fitting curve and the light spot intensity data, the laser integral light intensity distribution uniformity detection in the time dimension is realized in combination with the sampling time, and the rapid dynamic detection of the laser integral light intensity distribution uniformity is achieved.

The application relates to a variable-depth power equipment lightweight identification method, and belongs to the fields of power equipment defect identification and computer vision. The method comprises the following steps: constructing a multi-dimensional joint coding image complexity representation method to obtain a feature map of power image data; and constructing a variable-depth power equipment lightweight identification method to realize self-adaptive identification of power equipment based on the obtained feature map of the power image data. The application can adaptively adjust the model depth according to the image complexity, and effectively reduce the calculation and reasoning time.

The application discloses a large model cooperative road disease data efficient intelligent labeling method, and particularly relates to the technical field of road detection; the application extracts various core features from the disease ontology and the recognizable dimension, comprehensively covers the complexity of the disease itself and the complexity of the recognition difficulty, avoids the limitation of single feature, quantifies the two types of complexity through the disease evaluation coefficient and the disturbance evaluation coefficient, and then fuses them into a complex evaluation coefficient to complete the complexity determination according to the coefficient interval division level, so that the problem that the existing technology mainly adopts a fixed model full quantity calling mode, and the determination of the image complexity mainly depends on a single feature and ignores the cooperative influence of the disease ontology feature and the environmental interference feature is solved.

The invention relates to a treatment plan determination method, device and system. The method comprises the following steps: acquiring an initial scanning image for a scanning object; determining an initial target region sketching result and an initial treatment plan for the region of interest based on the initial scanning image; comparing the image complexity of the current scanning image of the region of interest with a degree threshold; when it is determined that the image complexity of the current scanning image is smaller than a preset degree threshold value, the initial target region delineation result and the initial treatment plan serve as a target target region delineation result and a target treatment plan; or, when it is determined that the image complexity of the current scanning image is larger than or equal to the degree threshold value, the modification of the initial target region sketching result is determined, and the target constraint is adjusted based on the current scanning image according to the modified initial target region sketching result so as to determine the target treatment plan. According to the invention, while the treatment effect is ensured, the time spent in the radiotherapy process is effectively shortened.

The invention relates to a video coding rate control method and device, electronic equipment and a storage medium. The method comprises the following steps: dividing a video image into a plurality of sub-images; dynamically allocating a code rate of a low-frequency sub-band and a code rate of a high-frequency sub-band in the sub-image according to the image complexity factor of each sub-image in the plurality of sub-images; respectively setting initial quantization parameter values of a macro block level for a low-frequency sub-band and a high-frequency sub-band in the sub-graph; and dynamically adjusting the quantization parameters of the low-frequency sub-band and the high-frequency sub-band in the sub-graph according to the code rate of the low-frequency sub-band and the code rate of the high-frequency sub-band in the pre-allocated sub-graph and the initial quantization parameter value of the macro block level. According to the invention, the coding efficiency and quality in a high-resolution real-time video coding scene can be improved.

This application discloses a video encoding bitrate control method, apparatus, electronic device, and storage medium. The method includes: acquiring the current image frame of a video stream to be processed according to a preset time period; determining each first target region and the image complexity of each first target region of the current image frame according to a preset algorithm; inputting each first target region, the image complexity of each first target region, the target quantization parameter set of the previous image frame, the bitrate of the previous image frame, and the perceived image quality evaluation value of the previous image frame into a parameter fitting model to obtain a preset number of quantization parameter sets for the current image frames; determining the target quantization parameter set of the current image frame based on the preset number of quantization parameter sets of the current image frames and the perceived quality evaluation model; and encoding the current image frame and image frames in the next time period according to the target quantization parameter set of the current image frame.

This invention provides a medical image retrieval method based on fuzzy hashing networks, solving the technical problems of image complexity, uncertainty, and data imbalance faced by current deep hashing algorithms in medical image retrieval. The technical solution is as follows: First, a medical image database is established and divided into a test set and a training set; second, a fuzzy hashing network is constructed based on fuzzy rules; then, the hash center loss, balance loss, quantization loss, and classification loss are calculated; third, the network parameters are optimized using an alternating learning algorithm based on the loss function; finally, images are read from the test set, similar images are retrieved from the database, and the average retrieval accuracy is calculated. The beneficial effects of this invention are: enhanced ability to handle uncertain information and class imbalance in medical images, improving the accuracy of medical image retrieval.

The invention belongs to the technical field of image transmission and intelligent processing, and particularly relates to an intelligent block image transmission system for picture processing, which comprises the following steps: firstly, executing non-redundancy preprocessing on an input image to eliminate image interference and identify a key area; obtaining the texture complexity and criticality level of each region through sub-region division and feature calculation, so as to generate an image complexity-criticality map; taking the image complexity-criticality map as a basis, abandoning a fixed blocking mode, and making a differential blocking rule according to regional features; meanwhile, key degree, size and position labels are added to the blocks; based on this, the texture complexity and criticality of different regions of the image are extracted through image preprocessing, and then the differentiated blocking rule is formulated based on the features, so that the method has the advantages of realizing depth adaptation of the blocking strategy and the image content features, and avoiding detail loss of the key region and redundant data transmission of the non-key region caused by fixed blocking.

The invention relates to the technical field of computer vision, and provides a fundus image segmentation method and network based on image complexity, and the method comprises the steps: obtaining a to-be-processed fundus image, and analyzing the global features of the to-be-processed fundus image through a complexity analysis module, so as to generate a complexity parameter vector, the global features at least comprise contrast, texture complexity and noise distribution; meanwhile, multi-level feature extraction is carried out on the processed eye fundus image through an encoder, so that encoding features of different levels are generated; performing feature fusion based on the complexity parameter vector and the coding features of different levels through a decoder to generate fused features; and outputting a segmentation result based on the fusion feature. And feature fusion is guided through a complexity parameter vector, so that the segmentation method can adapt to fundus images with different complexities, and the accuracy and robustness of a segmentation result are improved.