187 results about "JPEG" patented technology

The invention relates to the technical field of image restoration, in particular to a spatial frequency hybrid restoration method for JPEG (Joint Photographic Experts Group) compressed blurred image restoration, which is characterized in that a spatial frequency hybrid restoration network is constructed for compressed blurred image restoration and comprises three coding blocks and three decoding blocks, each of the coding block and the decoding block is composed of a plurality of spatial frequency mixing blocks; the spatial frequency mixing block comprises a spatial frequency double-branch mixing structure and an ISS block; the SFHRN provided by the invention can realize a more efficient restoration effect on the compressed blurred image with lower model parameter quantity and calculation complexity.

The invention relates to the technical field of information encryption and decryption, in particular to an image information encryption and decryption method and software system based on a digital image steganography technology. According to the technical scheme, the method comprises the following steps that a carrier image format and a to-be-processed information type are determined, the carrier image format comprises a PNG image and a JPEG image, and the to-be-processed information type comprises text information, picture information and audio information; encrypting the plaintext information to generate ciphertext information; and selecting a corresponding steganography path according to the format of the carrier image, embedding the ciphertext information into the carrier image, generating an encrypted image, and generating a key file recording the embedded related information. According to the method, various image formats and information types are supported, the compatibility, safety, robustness and practicability of information encryption and decryption are improved through key encryption, face binding verification and a compensation mechanism for JPEG, and the method is suitable for scenes such as information protection and private transmission.

The invention relates to the technical field of power grid image classification, in particular to an image classification method based on a semantic knowledge base in a smart power grid, and the method comprises the following operation steps: S1, data collection: 1.1, collecting image data from various devices (such as a monitoring camera and unmanned aerial vehicle inspection equipment) of the smart power grid; the images may include the appearance of the power equipment, the operation state (such as the oil temperature indication of a transformer and the on-off state of a switch), and the integrity of the line. 1.2, preliminarily preprocessing the acquired images, including unifying the format of the images (for example, converting the images into a JPEG (Joint Photographic Experts Group) format), adjusting the size (enabling the images to adapt to a subsequent processing algorithm, for example, uniformly adjusting the images into 512 * 512 pixels) and removing noise (removing salt and pepper noise in the images by using a filtering technology, for example, Gaussian filtering); according to the method, the feature extraction process can be guided through the semantic knowledge base, so that the model focuses on key features related to semantics, and the classification precision can be improved.

The invention discloses a railway driver driving behavior monitoring method based on artificial intelligence. An artificial intelligence processing unit completes hardware interface initialization; the integrated wireless equipment of the railway locomotive sends a network IP address and equipment identification information of the main control end to a host process of the artificial intelligence processing unit; a host process of the artificial intelligence processing unit initiates a video stream pulling request to a digital camera at a corresponding end through an RTSP protocol; the main process of the artificial intelligence processing unit receives the RTSP code stream and then decodes the RTSP code stream into a picture in a JPEG format, the image is cut and zoomed, and complete metadata information is added for each picture; three reasoning processes of the artificial intelligence processing unit run in parallel, and each process performs targeted reasoning detection on the decoded JPEG picture and metadata based on a self-loaded model: a main process receives a reasoning detection result, performs comprehensive judgment in combination with a preset threshold value and an abnormal state duration, and sends the result to the main process; triple alarms of local reminding, driver informing and remote monitoring are realized, and the driving behavior of the driver is standardized.

A kind of railway driver driving behavior monitoring method based on artificial intelligence, artificial intelligence processing unit completes hardware interface initialization;Railway locomotive comprehensive wireless device sends the network IP address of main control end, device identification information to the main process of artificial intelligence processing unit;Artificial intelligence processing unit main process initiates video stream pull request to the digital camera of corresponding end by RTSP protocol;Artificial intelligence processing unit main process decodes into JPEG format picture after receiving RTSP code stream, trims zoom image, adds complete metadata information to each picture;Three inference processes of artificial intelligence processing unit run in parallel, each process is based on the model loaded by itself to decode the JPEG picture and metadata for targeted inference detection:Main process receives inference detection result, carries out comprehensive judgment in conjunction with preset threshold and abnormal state duration, realizes local reminder, driver notification, remote monitoring triple alarm, standardizes driver driving behavior.

Various systems and methods are presented regarding generating executable computer code / instructions from input files, whereby the input files may be an image file (e.g., JPEG, PDF, etc.). The image file can be digital capture of a sequence of instructions such as a graphical representation comprising a ladder diagram, a function block diagram, a sequential function chart, etc. P&ID and suchlike can also be submitted to the system. Code generated from the input files can be enhanced by application of historical data comprising pertinent subroutines, and suchlike. Further, an entity can be prompted to provide further information in the event of the input file does not provide all of the content.

The present application relates to the technical field of cloud computing, and specifically relates to a method for parallel decoding and rendering of a sequence of JPEG images. The method comprises the following step: on the basis of CPU concurrent programming, using a thread allocation policy to perform Huffman decoding on received JPEG images, so as to generate a sequence of frames processed in parallel. In the present application, by using CPU concurrent programming and a thread allocation policy, the efficiency of Huffman decoding is greatly improved and the time taken for preliminary processing is reduced; by means of independent decoding of threads, the throughput and efficiency are significantly improved; furthermore, in view of an isolation writing policy and a video memory allocation policy, data is efficiently written into a GPU video memory, thereby reducing data operations and increasing the speed; the parallel computing capability of a GPU makes IDCT and YUV-to-TGB conversion rapid and efficient, thereby reducing the delay and improving the output quality; by means of a shared video memory buffer policy, the utilization of hardware resources is optimized; and by using OpenGL rendering, the smoothness of rendering and the visual effect are ensured. On the basis of a combination of these optimizations, the speed and quality of image processing are significantly improved, thereby laying a solid foundation for real-time applications.

The application provides a JPEG data decoding method and device, equipment and storage medium, which can be used in the field of image processing. The method comprises the following steps: obtaining target JPEG data, marking the target JPEG data based on a preset segmentation length in units of target MCUs to obtain target JPEG segmented data; storing the target JPEG segmented data into a target SVM to obtain multiple initial decoded segments and synchronization parameter information; in response to the multiple initial decoded segments being correctly decoded, judging whether the number of target MCUs corresponding to the multiple initial decoded segments meets a preset number condition; if yes, sending a target decoding instruction to a target GPU to enable the target GPU to perform target Huffman decoding to obtain multiple target decoded segments; decoding each target DCU included in each target decoded segment to obtain target JPEG decoding data; and storing the target JPEG decoding data into the target SVM to obtain a target RGB image corresponding to the target JPEG decoding data based on the target GPU. The CPU running load is reduced, and the decoding efficiency is improved.

The invention discloses an image compression method based on a JPEG-LS (Joint Photographic Experts Group-Least Squares) parallel optimization algorithm, which comprises the following steps: calculating local gradient values of to-be-coded data and then merging to obtain a context index Q value, the to-be-coded data being a prediction error of a current pixel; grouping the data to be coded according to the context index Q value; if the to-be-coded number contained in the key group affects the parallelism degree, a controllable distortion value is introduced into the pixel value of the to-be-coded data by using an equalization algorithm, and the group serial number corresponding to the data in the key group is corrected; deploying processing units of parallel channels with the number equal to that of the groups, and scheduling the grouped data to be coded to the processing units to realize pipeline processing; integrating each group of data into coded and compressed data through code stream splicing; the image compression method based on the JPEG-LS parallel optimization algorithm is used for image compression, the system data size is low, and the satellite-ground transmission bandwidth pressure is small.

The present invention relates to a method and device for active defense against deep fake face based on discrete wavelet transform, and to the field of image processing technology. The method comprises: using a deep fake model to edit the attributes of the original and disturbed images, calculating the mean square error loss and structural similarity loss between the two, and merging them into a total loss function after assigning different weights; obtaining gradient information based on the back propagation of the loss function, determining the optimal direction of the disturbance application, and forming an intermediate disturbance value; embedding low-frequency and high-frequency components into the corresponding components of the disturbed image through discrete wavelet transform, and then reconstructing the image through inverse discrete wavelet transform; checking whether the number of iterations has been reached, if not, introducing a JPEG noise layer to continue iteratively adjusting the disturbance; if so, outputting a face image containing invisible disturbance information. The present invention makes full use of the robustness in the wavelet domain, and improves the resistance of disturbance to JPEG compression while ensuring that the disturbance is invisible.

There is provided a pixel transform method for JPEG / MPEG compression of double phase-amplitude coded holograms. According to an embodiment, a hologram compression method transforms a hologram represented by a complex number into a hologram represented only by a phase, rearranges pixels of the transformed hologram, and compresses the rearranged hologram. Pixels of a DPAC hologram are rearranged before the DPAC hologram is compressed in a standardized image / video compression technique, so that a high frequency component is prevented from occurring when a hologram is compressed. Accordingly, hologram quality degradation occurring when a DPAC hologram is used in a standardized image / video compression technique can be prevented.

The invention provides a pathological section streaming reading method and device and a readable storage medium, and the method comprises the steps: constructing a hierarchical tile storage structure, dividing a pathological section image into multiple layers of tiles according to a preset scaling, and generating hierarchical pointer information; a target tile index is dynamically calculated according to the hierarchy and coordinates of the user visible area, and on-demand streaming loading is achieved; the data access efficiency is optimized through a cache management module, and expired cache is automatically cleaned based on an access time threshold value; tile data can be stored in compressed formats such as JPEG and HEVC, and the storage and transmission cost is reduced. According to the method, the problems of high network delay, large storage redundancy and poor protocol adaptability caused by full-amount downloading of pathological sections in a cloud storage environment are solved, and the access efficiency and resource utilization rate of medical images are remarkably improved.

The invention relates to the field of sign language translation. The invention discloses a sign language real-time translation system based on multi-dimensional data fusion. The sign language real-time translation system comprises an image information acquisition module, a data processing module and a result display module, the image information acquisition module captures sign language actions of a user in real time through a camera to generate sign language videos, and operates four core task architectures including an HTTP server task, a camera acquisition task, a streaming media transmission task and a video downloading task on the basis of FreeRTOS, the HTTP server task receives a client request, and the video downloading task receives the client request. Comprising an MJPEG video stream, a JPEG single frame capture and a video downloading access endpoint, a built-in CORS cross-domain access support, a camera acquisition task continuously captures JPEG frames with a fixed frame rate, each task is connected with a queue transfer client through semaphore protection frame data to realize efficient collaboration, multi-task parallel processing is realized, the transmission frame rate is accurately controlled to meet the algorithm requirement, and the algorithm requirement is met. And ESP32 dual-core resources are utilized to the maximum extent.

An image steganography analysis method based on a hybrid deep learning framework combines the feature extraction advantage of a JPEG steganography rich model and the integratable advantage of a deep learning model to construct a multi-stage hybrid deep learning JPEG steganography analysis framework, and the framework has expandability and can be applied to image steganography analysis. The latest deep learning steganalysis network can be added to the framework. Meanwhile, the steganalysis problem under various complex scenes such as data source mismatching can also be adapted; a color image steganalysis CNN trunk model formed by three stages of preprocessing, feature extraction and feature classification is designed, and a space attention mechanism and a channel attention mechanism are added in a trunk stream, so that the detection accuracy is improved; a new algorithm optimization module is designed based on the framework, the network structure is adjusted, and the generalization ability, convergence performance, detection accuracy and robustness of the model are further improved.

The invention discloses a Grafana-based video monitoring method, system and device, and belongs to the technical field of industrial Internet of Things, and the system comprises an industrial personal computer, go2rtc software and Grafana software. The industrial personal computer is used for providing a running environment for the go2rtc software and the Grafana software; the go2rtc software converts a video stream into a WebRTC video stream through protocols such as RTSP, MJPEG, JPEG and RTMP; and the Grafana software is responsible for analyzing the WebRTC video stream and publishing the WebRTC video stream to a Web server, and a user remotely accesses a visual interface through a Web browser. The problems that video monitoring of an existing monitoring system is difficult to integrate with other service systems, video picture delay is high, cross-platform access is limited, maintenance cost is high, and deployment platforms are limited are solved, and digital transformation of enterprises is accelerated.

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

A diffusion model is augmented with pseudoinverse guidance to restore data, removing artifacts and generating high-quality reconstructed data from limited, low-quality and / or noisy input data. The low-quality input data is denoised by a diffusion model and the denoised input data is combined with a guidance term to produce output data of higher-quality compared with the low-quality input data. The guidance term is a vector-Jacobian product that encourages consistency between the denoised input data and measurements after a pseudoinverse transformation. The denoising process may be applied in an iterative fashion to generate valid solutions to the inverse problem. The augmented diffusion model is a problem-agnostic (e.g., plug-and-play) denoiser that can restore data for a variety of tasks. Example image restoration tasks include denoising, JPEG denoising, deblurring, outpainting, inpainting, colorization, high-dynamic range, and super-resolution.

A system and method are presented for recognizing position of a person's body appendage such as fingers using electrocardiogram data. An electrocardiogram (ECG) sensor placed on a user is in real time communication with a computing device containing a convolutional neural network (CNN) trained to receive real time ECG data and predict a position of the person's body appendage. The real time ECG sensor data is processed from digital format into an analog signal to form ECG image data. The ECG data may be converted from comma separated values (.csv) to joint photographic experts group (.jpeg) format. The output of the CNN model predicts the user appendage position from among a plurality of classified body appendage positions, such as number of fingers extended. The CNN model may include an input layer, convolutional 2D layer, batch normalization layer, rectified linear units layer, fully connected layer, softmax layer, and classification layer.

The invention relates to the field of image lossless coding, and provides an enhanced QOI encoder realized based on an FPGA. The design is based on an enhanced QOI encoding strategy, and the encoder consists of an input bit width conversion module, a JPEG-LS predictor, an enhanced QOI encoding unit, an encoding group FIFO module, a compact bit width conversion module and a keep signal specification module, and has the characteristics of higher throughput rate, good time sequence performance and lower hardware resource consumption. A core component JPEG-LS predictor and an enhanced QOI coding unit adopt pipeline design, and two pixels can be coded in each clock period; coding of images with any resolution ratio is supported (only the image width is required to be an even number); 64-bit AXI Stream bus interfaces are adopted for input pixel streams and output coded data streams, and coded data output does not carry null bytes, so that DMA (direct memory access) transmission is facilitated.

A system and method for energy-efficient approximate digital JPEG and MJPEG-compression. The system includes a controller unit to control a processing loop for processing image blocks based on a comparison of a current image block to a previous image block. The system includes a quantization unit configured to quantize the frequency domain representation using an approximate quantization process and a quantization (Q) matrix. The quantization unit is configured to: identify, a nearest power of two value for each element of the quantization matrix; generate an updated Q matrix by assigning each element of the quantization matrix with the identified nearest power of two value; and shift each element of the updated Q matrix by a number of bits to generate a quantized frequency domain representation. The number of bits corresponds to the identified nearest power of two for the corresponding element of the updated Q matrix.

The application provides a method for detecting synchronous JPEG recompression of BMP format concealed images, and a method for effectively estimating a JPEG quantization table is provided; by using the particularity of JPEG 100 quality factor compression, the minimum error caused by JPEG compression when the quality factor is 100 is extracted to construct an expected-minimum error feature; by using the convergence characteristics of inverse quantization JPEG coefficients, a first-order relative error feature is proposed, and finally synchronous JPEG recompression images saved in BMP format are effectively detected, and synchronous JPEG recompression at any quality factor can also be detected. The application can effectively distinguish single compressed images from recompressed images, and has higher accuracy compared with corresponding traditional JPEG recompression detection methods.

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

The invention relates to a non-standard JPEG (Joint Photographic Experts Group) coding processing method for dynamic code rate control. The method comprises the following steps: presetting a multi-frame joint image processing scene, obtaining an original reference image, and carrying out combined processing of boundary expansion and random bias addition to obtain a preprocessed reference image; dividing macro blocks for the preprocessed reference image, and distributing an independent coding parameter configuration space after verification and compliance; frequency domain conversion is executed according to a division result, an initial quantization parameter is calculated through an index model, and a dynamic quantization table is dynamically adjusted; executing non-standard entropy coding to obtain an indexed macro block coding code stream; combining the indexed code stream and a macro block division result to construct an index table containing a macro block serial number, an initial address and a code stream length; analyzing the code stream to obtain quantized data, and performing inverse quantization by using a dynamic quantization table to obtain spatial domain data; enabling the format of the reconstructed image to be consistent with that of the preprocessed reference image through reverse preprocessing, and performing real-time processing on the linkage input image to obtain a reconstructed reference image; and non-standard JPEG coding processing of dynamic code rate control is realized.

This article 11 is a fixture for detachably attaching a first article and a second article. As shown in Reference FIG. 1 and Reference FIG. 2 (a cross-sectional view taken along CC in Reference FIG. 1), this article 11 includes a plate- or sheet-like base portion 21, an engagement portion 31 attached (e.g., by adhesive, sticking, or fusion) to one surface 22 of the two main surfaces 22, 23 of the base portion 21, and an attraction portion 41 (here, a permanent magnet) embedded in the base portion 21 in an annular shape (along a circle on the one surface 22, centered on a central axis K, which will be described later). The engagement portion 31 includes a disk-shaped engagement main body portion 33 (both main surfaces of which are plate-shaped and form a circle centered on the central axis K) and three claw portions 35a, 35b, and 35c formed to protrude from the outer periphery of the engagement main body portion 33. This article 11 can be detachably engaged with a receiver. As shown in Reference Figure 3 and Reference Figure 4 (viewed from the opposite direction to Reference Figure 3), an example receiver 71 is formed of an annular member. Reference Figure 5 is a cross-sectional view of Reference Figure 3 taken along line AA, and Reference Figure 6 is an enlarged view of circle F in Reference Figure 5. Reference Figure 7 is a cross-sectional view of Reference Figure 3 taken along line BB, Reference Figure 8A is an enlarged view of circle G in Reference Figure 7, and Reference Figure 8B is an enlarged view of circle H in Reference Figure 7. As shown in Reference Figure 9 showing the structure (which shows the receiver 71 viewed from the same direction as Reference Figure 4 showing the structure), the receiver 71 has a first inner surface 75 (forming the side of a right circular cylinder (radius r1) with a central axis J perpendicular to Reference Figure 9 showing the structure) formed on the bottom surface 73 side, and a second inner surface 77 (forming the side of a right circular cylinder (radius r2) with a central axis J as its axis) formed on the surface 74 (the surface that appears in Reference Figure 3 showing the structure) side opposite the bottom surface 73 (the surface that appears in Reference Figure 3 showing the structure). The parts other than those painted in black are the parts for which design registration is sought. This design is represented by a drawing substitute sample or model, so please refer to the original for details and colors.View showing front, top and right side of sample JPEG 0001812574000001.jpg 113 150 View showing back, bottom and left side of sample JPEG 0001812574000002.jpg 113 150 Front view of sample JPEG 0001812574000003.jpg 113 150 Back view of sample JPEG 0001812574000004.jpg 113 150 Right side view of sample JPEG 0001812574000005.jpg 113 150 Left side view of sample JPEG 0001812574000006.jpg 113 150 Top view of sample JPEG 0001812574000007.jpg 113 150 Bottom view of sample JPEG 0001812574000008.jpg 113 150 Reference diagram 1 showing structure JPEG 0001812574000009.jpg 53 56 Reference diagram 2 showing structure JPEG 0001812574000010.jpg 34 64 Reference diagram 3 showing structure JPEG 0001812574000011.jpg 41 43 Reference diagram 4 showing structure JPEG 0001812574000012.jpg 31 41 Reference diagram 5 showing structure JPEG 0001812574000013.jpg 40 24 Reference diagram 6 showing structure JPEG 0001812574000014.jpg 34 21 Reference diagram 7 showing the structure JPEG 0001812574000015.jpg 23 43 Reference diagram 8A showing the structure JPEG 0001812574000016.jpg 16 38 Reference diagram 8B showing the structure JPEG 0001812574000017.jpg 20 37 Reference diagram 9 showing the structure JPEG 0001812574000018.jpg 46 49 Reference diagram 1 showing the usage state JPEG 0001812574000019.jpg 73 76 Reference diagram 2 showing the usage state JPEG 0001812574000020.jpg 30 64 Reference diagram 3 showing the usage state JPEG 0001812574000021.jpg 74 83 Reference diagram 4 showing usage status JPEG 0001812574000022.jpg 32 64.

A technique for improving progressive encoded JPEG includes displaying an oversmoothed version of an image as the image data is being received. The oversmoothed image may be smoothed according to a smoothing kernel, e.g., a convolution kernel (such as a Gaussian). The oversmoothed image is a first layer over which other image layers are displayed. It is noted that the oversmoothed image may present a recognizable version of the image to a user, including recognizable versions of various image features (e.g., persons, objects). As the other layers are rendered on the display, these image features remain visible to the user. That is, the image features are not artifacts that may disappear with the rendering of final image layers; this may occur with the conventional progressive encoded images and interferes with the user experience.

This article 11 is a fixture for detachably attaching a first article and a second article. As shown in Reference FIG. 1 and Reference FIG. 2 (a cross-sectional view taken along CC in Reference FIG. 1), this article 11 includes a plate- or sheet-like base portion 21, an engagement portion 31 attached (e.g., by adhesive, sticking, or fusion) to one surface 22 of the two main surfaces 22, 23 of the base portion 21, and an attraction portion 41 (here, a permanent magnet) embedded in the base portion 21 in an annular shape (along a circle on the one surface 22, centered on a central axis K, which will be described later). The engagement portion 31 includes a disk-shaped engagement main body portion 33 (both main surfaces of which are plate-shaped and form a circle centered on the central axis K) and three claw portions 35a, 35b, and 35c formed to protrude from the outer periphery of the engagement main body portion 33. This article 11 can be detachably engaged with a receiver. As shown in Reference Figure 3 and Reference Figure 4 (viewed from the opposite direction to Reference Figure 3), an example receiver 71 is formed of an annular member. Reference Figure 5 is a cross-sectional view of Reference Figure 3 taken along line AA, and Reference Figure 6 is an enlarged view of circle F in Reference Figure 5. Reference Figure 7 is a cross-sectional view of Reference Figure 3 taken along line BB, Reference Figure 8A is an enlarged view of circle G in Reference Figure 7, and Reference Figure 8B is an enlarged view of circle H in Reference Figure 7. As shown in Reference Figure 9 showing the structure (which shows the receiver 71 viewed from the same direction as Reference Figure 4 showing the structure), the receiver 71 has a first inner surface 75 (forming the side of a right circular cylinder (radius r1) with a central axis J perpendicular to Reference Figure 9 showing the structure) formed on the bottom surface 73 side, and a second inner surface 77 (forming the side of a right circular cylinder (radius r2) with a central axis J as its axis) formed on the surface 74 (the surface that appears in Reference Figure 3 showing the structure) side opposite the bottom surface 73.This design is represented by a drawing substitute sample or template, so please refer to the original for details and colors.View showing front, top and right side of sample JPEG 0001812573000001.jpg 113 150 View showing back, bottom and left side of sample JPEG 0001812573000002.jpg 113 150 Front view of sample JPEG 0001812573000003.jpg 113 150 Back view of sample JPEG 0001812573000004.jpg 113 150 Right side view of sample JPEG 0001812573000005.jpg 113 150 Left side view of sample JPEG 0001812573000006.jpg 113 150 Top view of sample JPEG 0001812573000007.jpg 113 150 Bottom view of sample JPEG 0001812573000008.jpg 113 150 Reference diagram 1 showing structure JPEG 0001812573000009.jpg 53 56 Reference diagram 2 showing structure JPEG 0001812573000010.jpg 34 64 Reference diagram 3 showing structure JPEG 0001812573000011.jpg 41 43 Reference diagram 4 showing structure JPEG 0001812573000012.jpg 31 41 Reference diagram 5 showing structure JPEG 0001812573000013.jpg 40 24 Reference diagram 6 showing structure JPEG 0001812573000014.jpg 34 21 Reference diagram 7 showing the structure JPEG 0001812573000015.jpg 23 43 Reference diagram 8A showing the structure JPEG 0001812573000016.jpg 16 38 Reference diagram 8B showing the structure JPEG 0001812573000017.jpg 20 37 Reference diagram 9 showing the structure JPEG 0001812573000018.jpg 46 49 Reference diagram 1 showing the usage state JPEG 0001812573000019.jpg 73 76 Reference diagram 2 showing the usage state JPEG 0001812573000020.jpg 30 64 Reference diagram 3 showing the usage state JPEG 0001812573000021.jpg 74 83 Reference diagram 4 showing usage status JPEG 0001812573000022.jpg 32 64.

Various systems and methods are presented regarding generating executable computer code / instructions from input files, whereby the input files may be an image file (e.g., JPEG, PDF, etc.). The image file can be digital capture of a sequence of instructions such as a graphical representation comprising a ladder diagram, a function block diagram, a sequential function chart, etc. P&ID and suchlike can also be submitted to the system. Code generated from the input files can be enhanced by application of historical data comprising pertinent subroutines, and suchlike. Further, an entity can be prompted to provide further information in the event of the input file does not provide all of the content.

A system and method for energy-efficient approximate digital JPEG and MJPEG-compression. The system includes a controller unit to control a processing loop for processing image blocks based on a comparison of a current image block to a previous image block. The system includes a quantization unit configured to quantize the frequency domain representation using an approximate quantization process and a quantization (Q) matrix. The quantization unit is configured to: identify, a nearest power of two value for each element of the quantization matrix; generate an updated Q matrix by assigning each element of the quantization matrix with the identified nearest power of two value; and shift each element of the updated Q matrix by a number of bits to generate a quantized frequency domain representation. The number of bits corresponds to the identified nearest power of two for the corresponding element of the updated Q matrix.

The application discloses a method for urine test paper mobile phone image detection and analysis, which comprises the following steps: collecting urine test paper JPEG images under multiple light sources under a standard light source box, taking the test paper image under the D65 light source as a standard image, converging the RGB color performance of the test paper block in the corresponding area of the test paper image under the standard light source to the test paper image under the remaining light source environment, that is, training and obtaining a compensation model, fully considering the test paper color features and the mobile phone camera characteristic features in the model characteristic parameters, ensuring the accuracy of the color correction model by analyzing the maximum value, minimum value and variance of the features during preprocessing, establishing a training TabNet model, fusing multiple regression models such as LightGBM, ElasticNet Regression and Support Vector Regression to optimize the color correction effect, and realizing a strengthened compensation model. Through the color correction of the test paper image by the compensation model, the error caused by the color performance of the urine test paper image affected by the environmental light is reduced, the most real color of the test paper image is restored, the color recognition degree is improved, the accuracy of the test paper detection and analysis is improved, and the model is small and has a fast recognition speed.

The present application belongs to the field of image processing and target detection, and specifically provides a RAW image processing method for target detection, which is used to improve the compression efficiency of the image, and can significantly improve the recognition accuracy when the processed image is applied to the target detection task. The present application constructs a semantic fusion network to directly generate an image from the RAW image, which is more suitable for detection and occupies less storage. In the present application, the RAW image retains a large amount of original information, and the semantic information is obtained through the JPEG image and the label provided thereby, which is integrated into the RAW image processed by the ISP. While taking into account the detection accuracy, the required number of bits for image transmission is effectively reduced. At the same time, an efficient and close-to-training-target loss function is used to constrain the entire training process, which can significantly reduce the space occupied by the image and improve the detection rate. In summary, after introducing the RAW image processing method for target detection, the present application can improve the recognition accuracy and reduce the demand for storage space.