ENCODING AND DECODING IMAGES USING DIFFERENTIABLE JPEG COMPRESSION

    公开(公告)号:US20250008132A1

    公开(公告)日:2025-01-02

    申请号:US18755150

    申请日:2024-06-26

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

    System for application self-optimization in serverless edge computing environments

    公开(公告)号:US11847510B2

    公开(公告)日:2023-12-19

    申请号:US17964170

    申请日:2022-10-12

    CPC classification number: G06F9/543 G06F9/505

    Abstract: A method for implementing application self-optimization in serverless edge computing environments is presented. The method includes requesting deployment of an application pipeline on data received from a plurality of sensors, the application pipeline including a plurality of microservices, enabling communication between a plurality of pods and a plurality of analytics units (AUs), each pod of the plurality of pods including a sidecar, determining whether each of the plurality of AUs maintains any state to differentiate between stateful AUs and stateless AUs, scaling the stateful AUs and the stateless AUs, enabling communication directly between the sidecars of the plurality of pods, and reusing and resharing common AUs of the plurality of AUs across different applications.

    VIDEO CLASSIFIER
    3.
    发明公开
    VIDEO CLASSIFIER 审中-公开

    公开(公告)号:US20230237805A1

    公开(公告)日:2023-07-27

    申请号:US18157915

    申请日:2023-01-23

    CPC classification number: G06V20/56 B60W30/09 G06V10/761 G06V10/764 G06V20/46

    Abstract: A computer-implemented method is provided. The method includes classifying a video clip of consecutive video frames into one of predefined new classes in relation to a base training set class. The method further includes controlling a system of a motor vehicle for accident avoidance responsive to the one of the predefined classes indicating an impending collision. The classifying step includes extracting video frame features from the video clip. The classifying step further includes aggregating the video frame features of the consecutive video frames into a single frame feature to form a video level feature presentation. The classifying step also includes mapping, by a distance-based classifier, the video level feature presentation into a classification prediction based on cosine similarity.

    Automatically filtering out objects based on user preferences

    公开(公告)号:US11423262B2

    公开(公告)日:2022-08-23

    申请号:US16522711

    申请日:2019-07-26

    Abstract: A method is provided for classifying objects. The method detects objects in one or more images. The method tags each object with multiple features. Each feature describes a specific object attribute and has a range of values to assist with a determination of an overall quality of the one or more images. The method specifies a set of training examples by classifying the overall quality of at least some of the objects as being of an acceptable quality or an unacceptable quality, based on a user's domain knowledge about an application program that takes the objects as inputs. The method constructs a plurality of first-level classifiers using the set of training examples. The method constructs a second-level classifier from outputs of the first-level automatic classifiers. The second-level classifier is for providing a classification for at least some of the objects of either the acceptable quality or the unacceptable quality.

    Content-level anomaly detector for systems with limited memory

    公开(公告)号:US10740212B2

    公开(公告)日:2020-08-11

    申请号:US15970398

    申请日:2018-05-03

    Abstract: Systems and methods for implementing content-level anomaly detection for devices having limited memory are provided. At least one log content model is generated based on training log content of training logs obtained from one or more sources associated with the computer system. The at least one log content model is transformed into at least one modified log content model to limit memory usage. Anomaly detection is performed for testing log content of testing logs obtained from one or more sources associated with the computer system based on the at least one modified log content model. In response to the anomaly detection identifying one or more anomalies associated with the testing log content, the one or more anomalies are output.

    Structure-level anomaly detection for unstructured logs

    公开(公告)号:US10740170B2

    公开(公告)日:2020-08-11

    申请号:US15830579

    申请日:2017-12-04

    Abstract: A computer-implemented method, computer program product, and computer processing system are provided. The method includes preprocessing, by a processor, a set of heterogeneous logs by splitting each of the logs into tokens to obtain preprocessed logs. Each of the logs in the set is associated with a timestamp and textual content in one or more fields. The method further includes generating, by the processor, a set of regular expressions from the preprocessed logs. The method also includes performing, by the processor, an unsupervised parsing operation by applying the regular expressions to the preprocessed logs to obtain a set of parsed logs and a set of unparsed logs, if any. The method additionally includes storing, by the processor, the set of parsed logs in a log analytics database and the set of unparsed logs in a debugging database.

    Automated Anomaly Detection Service on Heterogeneous Log Streams

    公开(公告)号:US20170139806A1

    公开(公告)日:2017-05-18

    申请号:US15352546

    申请日:2016-11-15

    CPC classification number: G06F11/3612 G06F11/0706 G06F11/0766 G06F11/3636

    Abstract: Systems and methods are disclosed for handling log data from one or more applications, sensors or instruments by receiving heterogeneous logs from arbitrary/unknown systems or applications; generating regular expression patterns from the heterogeneous log sources using machine learning and extracting a log pattern therefrom; generating models and profiles from training logs based on different conditions and updating a global model database storing all models generated over time; tokenizing raw log messages from one or more applications, sensors or instruments running a production system; transforming incoming tokenized streams are into data-objects for anomaly detection and forwarding of log messages to various anomaly detectors; and generating an anomaly alert from the one or more applications, sensors or instruments running a production system.

    SYSTEM FOR APPLICATION SELF-OPTIMIZATION IN SERVERLESS EDGE COMPUTING ENVIRONMENTS

    公开(公告)号:US20230153182A1

    公开(公告)日:2023-05-18

    申请号:US17964170

    申请日:2022-10-12

    CPC classification number: G06F9/543 G06F9/505

    Abstract: A method for implementing application self-optimization in serverless edge computing environments is presented. The method includes requesting deployment of an application pipeline on data received from a plurality of sensors, the application pipeline including a plurality of microservices, enabling communication between a plurality of pods and a plurality of analytics units (AUs), each pod of the plurality of pods including a sidecar, determining whether each of the plurality of AUs maintains any state to differentiate between stateful AUs and stateless AUs, scaling the stateful AUs and the stateless AUs, enabling communication directly between the sidecars of the plurality of pods, and reusing and resharing common AUs of the plurality of AUs across different applications.

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