15 results about "Average bitrate" patented technology

A method and device estimate available transmission bandwidth of a network connection used to deliver a data file within a predetermined transmission time. A file size is obtained and a minimum average bitrate required to meet the time is computed as size divided by time. The transmission window is partitioned into alternating first and second time intervals. During each first interval, portions of the file are sent at a bitrate greater than the minimum average. During each second interval, remaining portions are sent at a lower bitrate selected such that the overall average bitrate across the window is at least the minimum average and below a configured bitrate threshold. Feedback (e.g., acknowledgments, loss, delay, or throughput) collected during the higher-rate first intervals is used to estimate the available bandwidth of the connection. The estimate may be used to adjust subsequent interval rates, file scheduling, or admission decisions.

The application provides a video data compression method in a vehicle networking scene, and relates to the technical field of image compression. A double-branch structure is designed in an image encoder, that is, a branch of a general feature extraction model and a branch of a vehicle networking feature extraction model, so as to solve the problem that a model trained by an existing learning type image compression algorithm does not match the characteristics of video data in the vehicle networking field, and the quality of decompressed video data is significantly improved. The application uses a lightweight fine-tuning technology, fine-tunes parameters of the general feature extraction model based on an existing vehicle networking public data set, so that a model suitable for the characteristics of vehicle networking video data can be quickly trained, the amount of parameters to be learned is greatly reduced, and the model training efficiency is improved. The hybrid architecture designed by the application can capture local texture features and global context dependency of an image, significantly reduces the average bit rate of pixels, and improves the storage efficiency of vehicle networking video data.

The invention relates to segment quality oriented adaptive stream creation. Embodiments are provided that provide improved stream generation. A video including a plurality of segments and a coding step specifying a plurality of maximum average bit rates (MABs) are received. A plurality of intermediate bit rates interspersed between the plurality of MABs is selected. A target average bitrate (TAB) segment is generated by encoding the first segment using the first MAB, and a first intermediate average bitrate (IAB) segment is generated by encoding the first segment using the first intermediate bitrate. A mass fraction is generated for the first TAB fragment and the first IAB fragment. A first output fragment is selected for a first fragment of the first MAB based on the mass fraction, wherein the first output fragment is a first TAB fragment or a first IAB fragment. Upon receiving a request for a first segment of the first MAB, a first output segment is output.

Methods, apparatus, systems and articles of manufacture are disclosed to determine virtual WiFi data rate. An example disclosed herein includes a control frame receiver to capture control frames from a user device. The example disclosed herein further includes a bitrate calculator to calculate a bitrate for each control frame by dividing a bitmap of a control frame by an amount of time between the control frame and a previously captured control frame. The example disclosed herein further includes an interval timer to control a length of time that the control frames are captured before calculating the virtual bandwidth. The example disclosed herein further includes a virtual bandwidth calculator to calculate the virtual bandwidth after the length of time, the virtual bandwidth calculated by dividing an average bitrate from the control frames by the length of time.

An apparatus including an interface and a processor. The interface can be configured to receive pixel data. The processor can be configured to process the pixel data arranged as a video frame, detect a bounding box location for a vehicle in the video frame, perform license plate detection within the bounding box location to detect a license plate region, determine a first encoding parameter, determine a second encoding parameter for the license plate region, and generate an encoded video frame using the first encoding parameter outside the license plate region and the second encoding parameter within the license plate region. An offset can be applied to the first encoding parameter to determine the second encoding parameter. The second encoding parameter can provide legibility of text while maintaining an average bitrate of the encoded video the same as encoding a full one of the video frames using the first encoding parameter.

A method and apparatus for estimating bitrate, buffering events and / or other QoE metrics of video reception, based on CDN logs, are provided. The method and apparatus may rely on an AI model, including a neural network. The neural network may receive a training data set comprised of a plurality of CDN server logs. The CDN server logs may be associated with known QoE metrics collected from a plurality of user devices. Once the neural network is trained, a CDN server log, without associated QoE metrics, may be received as input. With the trained neural network and the CDN server log, buffering events and average bitrate QoE metrics may be estimated for one or more user devices without explicitly receiving QoE metrics from the user device.

The present invention relates to segment quality-oriented adaptive streaming. Embodiments providing improved stream generation are provided. A video comprising a plurality of segments and an encoding ladder specifying a plurality of maximum average bitrates (MABs) are received. A plurality of intermediate bitrates interspersed between the plurality of MABs are selected. A target average bitrate (TAB) segment is generated by encoding a first segment using a first MAB, and a first intermediate average bitrate (IAB) segment is generated by encoding the first segment using a first intermediate bitrate. Quality scores are generated for the first TAB segment and the first IAB segment. A first output segment is selected for a first segment of the first MAB based on the quality score, wherein the first output segment is a first TAB segment or a first IAB segment. Upon receiving a request for a first segment of the first MAB, the first output segment is output.

The present application provides a method for obtaining an inbound bitrate, a method for processing a received stream, an electronic device, and a computer-readable storage medium. The method for obtaining an inbound bitrate includes: calculating an average bitrate corresponding to a current time interval; calculating a weight coefficient for a current bitrate fluctuation based on a previous inbound bitrate and the average bitrate corresponding to the current time interval; determining the current inbound bitrate and a next time interval based on the weight coefficient for the current bitrate fluctuation, and continuing to calculate the average bitrate corresponding to the next time interval after a preset time interval.

Methods, apparatus, systems and articles of manufacture are disclosed to determine virtual WiFi data rate. An example disclosed herein includes a control frame receiver to capture control frames from a user device. The example disclosed herein further includes a bitrate calculator to calculate a bitrate for each control frame by dividing a bitmap of a control frame by an amount of time between the control frame and a previously captured control frame. The example disclosed herein further includes an interval timer to control a length of time that the control frames are captured before calculating the virtual bandwidth. The example disclosed herein further includes a virtual bandwidth calculator to calculate the virtual bandwidth after the length of time, the virtual bandwidth calculated by dividing an average bitrate from the control frames by the length of time.

A method includes receiving transform coefficients corresponding to a scaled video input signal, the scaled video input signal including a plurality of spatial layers that include a base layer. The method also includes determining a spatial rate factor based on a sample of frames from the scaled video input signal. The spatial rate factor defines a factor for bit rate allocation at each spatial layer of an encoded bit stream formed from the scaled video input signal. The spatial rate factor is represented by a difference between a rate of bits per transform coefficient of the base layer and an average rate of bits per transform coefficient. The method also includes reducing a distortion for the plurality of spatial layers by allocating a bit rate to each spatial layer based on the spatial rate factor and the sample of frames.

An apparatus comprising an interface and a processor. The interface may be configured to receive pixel data. The processor may be configured to process the pixel data arranged as video frames, detect a bounding box location for a vehicle in the video frames, perform license plate detection within the bounding box location to detect a license plate region, determine first encoding parameters, determine second encoding parameters for the license plate region and generate encoded video frames using the first encoding parameters outside of the license plate region and the second encoding parameters within the license plate region. An offset may be applied to the first encoding parameters to determine the second encoding parameters. The second encoding parameters may provide clarity of text while keeping an average bitrate of the encoded video the same as encoding an entire one of the video frames using the first encoding parameters.

The invention provides a compression method for video data in an Internet of Vehicles scene, and relates to the technical field of image compression. A double-branch structure is designed in an image encoder, namely a branch of a general feature extraction model and a branch of an Internet of Vehicles feature extraction model; the problem that a model trained by an existing learning type image compression algorithm is not matched with video data characteristics in the field of Internet of Vehicles is solved, and the quality of the decompressed video data is remarkably improved. According to the method, the lightweight fine tuning technology is utilized, and the parameters of the general feature extraction model are finely tuned based on the existing Internet of Vehicles public data set, so that the model suitable for the Internet of Vehicles video data characteristics can be quickly trained, the parameter quantity to be learned is greatly reduced, the model training efficiency is improved, and the training efficiency is improved. The hybrid architecture designed by the invention can capture local texture features and global context dependency of the image, significantly reduces the average bit rate of pixels, and improves the storage efficiency of Internet of Vehicles video data.

Embodiments feature families of rate allocation and rate control methods that utilize advanced processing of past and future frame / field picture statistics and are designed to operate with one or more coding passes. At least two method families include: a family of methods for a rate allocation with picture look-ahead; and a family of methods for average bit rate (ABR) control methods. At least two other methods for each method family are described. For the first family of methods, some methods may involve intra rate control. For the second family of methods, some methods may involve high complexity ABR control and / or low complexity ABR control. These and other embodiments can involve any of the following: spatial coding parameter adaptation, coding prediction, complexity processing, complexity estimation, complexity filtering, bit rate considerations, quality considerations, coding parameter allocation, and / or hierarchical prediction structures, among others.

A method (400) includes receiving transform coefficients (226) corresponding to a scaled video input signal (120), the scaled video input signal (120) including a plurality of spatial layers (L), the plurality of spatial layers (L) including a base layer (L0). The method also includes determining a spatial rate factor (332) based on frame samples (S F ) from the scaled video input signal. The spatial rate factor defines a factor for bit rate allocation at each spatial layer of an encoded bitstream (204) formed from the scaled video input signal. The spatial rate factor is represented by a difference between a bit rate of each transform coefficient of the base layer and an average bit rate (R L ) of each transform coefficient. The method also includes reducing distortion of the plurality of spatial layers by allocating bit rates to each spatial layer based on the spatial rate factor and the frame samples.

1. Name of the product in this design: Configuration graphical user interface for intelligent front-end electronic device. 2. Purpose of this design: It is used for the connection and configuration of intelligent front-end electronic devices, with a focus on the compression and AI configuration of the connected video. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. The rear view, left view, right view, top view, and bottom view are omitted. 6. Purpose of the graphical user interface: Used for interfacing and configuring intelligent front-end electronic devices, with a focus on compressing the connected video and configuring AI. 7. Human-computer interaction method of graphical user interface: The product is carried out by intelligent front-end electronic devices. 8. Description of the changing states of the graphical user interface: The main view is a normal scene, where the encoding format, resolution, frame rate, and GOP can be configured. The compression level can be set, and the compression mode can be selected: intelligent sensing compression and image quality equalization compression. In the intelligent perception compression mode, you can select the type of perception target and the corresponding priority sorting, and it supports enabling the background extraction function and setting the audio delay. It allows you to preview connected videos or images and configure areas of interest. Clicking "Narrowband Scene" will switch to the interface shown in the interface change status diagram. The interface changes to a narrowband scene in the preset encoding configuration page, where you can configure the encoding format, resolution, frame rate, GOP, select image quality, and set the average bitrate and bitrate limit. Selectable compression modes: Intelligent Sensing Compression, Image Quality Balanced Compression. Supports enabling background removal and setting audio delay. It allows you to preview connected videos or images and configure areas of interest. In the interface, "XXX" represents variable numbers or text.