16 results about "Uncompressed video" patented technology

The invention provides an intelligent vehicle-mounted electronic rearview mirror system based on optical fiber transmission, and the system comprises a camera module which is installed outside a vehicle body; the host is connected with the camera module through an optical fiber and is used for receiving and processing the video data acquired by the camera module; and the rearview mirror display screen is connected with the host and is used for displaying the video processed by the host. Data transmission is carried out through the optical fiber, high-speed and uncompressed video signal transmission is ensured, delay and data loss are reduced, and the stability and reliability of the system are improved. The camera module comprises a self-adaptive optical assembly, the self-adaptive optical assembly comprises a wavefront detector, a wavefront controller and a wavefront corrector, and the self-adaptive optical assembly can correct subtle image distortion introduced by lens deformation or aerodynamic effect during high-speed driving, so that the imaging definition under the extreme illumination condition is further improved.

Systems, methods, and apparatuses are described for processing video. Encoded video data may be determined from uncompressed video data. A chrominance component for a portion of the encoded video data may be determined that references another portion of the encoded video data. A luma component of the portion of the encoded video data may be determined to correspond with the luma component of the corresponding portion of the uncompressed video data.

A system processing hardware executes a machine learning (ML) model-based video compression encoder to receive uncompressed video content and corresponding motion compensated video content, compare the uncompressed and motion compensated video content to identify an image space residual, transform the image space residual to a latent space representation of the uncompressed video content, and transform, using a trained image compression ML model, the motion compensated video content to a latent space representation of the motion compensated video content. The ML model-based video compression encoder further encodes the latent space representation of the image space residual to produce an encoded latent residual, encodes, using the trained image compression ML model, the latent space representation of the motion compensated video content to produce an encoded latent video content, and generates, using the encoded latent residual and the encoded latent video content, a compressed video content corresponding to the uncompressed video content.

Methods and systems are described to optimize a computation of a video quality metric. In one embodiment, a device receives a video clip, where the video clip includes a compressed video clip and an uncompressed video clip. The device may divide the video clip into a plurality of windows, wherein each of the windows includes a plurality of video frames. The device further may, for each of the plurality of windows, compute an estimated video quality error using a set of video frames selected from that window. Furthermore, if the estimated video quality error is greater than a threshold, the device may divide that window into a set of smaller windows and add the set of smaller windows to the plurality of windows, else the device may compute an estimated video quality score for that window.

A method for an online video game or application service system includes running a video game or application on an application host server at a data center, an uncompressed video stream being produced therefrom. The uncompressed video stream is encoded into compressed video stream, which is then transmitted over the Internet to an output client device of a user. The output client device decompresses the compressed video stream and displays live video on a screen. User control input transmitted from an input client device is delivered to the application host server. The user control input includes game or application commands. The input client device is associated with the user and is separate from the output client device. Responsive to receiving the game or application commands, the application host server generates a new uncompressed video stream.

A method of decoding compressed video or image data, or encoding uncompressed video or image data, using an 8-bit primary transform kernel matrix. The method can include determining whether to use a first transform kernel matrix having a first size type or a second transform kernel matrix having a second size type that is smaller than the first size type. When the result of the determination is to use the first transform kernel matrix, an 8-bit primary transform kernel matrix having a size of 64 points or more is used to encode or decode a target file. Otherwise, the method can include extracting the second transform kernel matrix of the second size type from the first transform matrix, and using the extracted second transform kernel matrix to encode or decode the target file.

A method of decoding compressed video or image data, or encoding uncompressed video or image data, using an 8-bit primary transform kernel matrix. The method can include determining whether to use a first transform kernel matrix having a first size type or a second transform kernel matrix having a second size type that is smaller than the first size type. When the result of the determination is to use the first transform kernel matrix, an 8-bit primary transform kernel matrix having a size of 64 points or more is used to encode or decode a target file. Otherwise, the method can include extracting the second transform kernel matrix of the second size type from the first transform matrix, and using the extracted second transform kernel matrix to encode or decode the target file.

The invention discloses an image transmission method of a non-compression packaging video and related equipment, and relates to the technical field of data transmission, the method comprises the following steps: acquiring a non-compression packaging original video file, video data in the original video file being in a non-4K alignment state; obtaining a storage address of each frame of image data in the original video file; based on the storage address of each frame of image data, the number of filling bytes to be inserted before the corresponding frame of image data is calculated, the corresponding number of filling bytes obtained through calculation are inserted, a target video file is generated, and video data in the target video file are in a 4K alignment state; and re-packaging the target video file into an uncompressed packaged video file, storing the uncompressed packaged video file, and then performing cache-free reading. According to the method and the device, the video data in the non-compression packaged video file can be efficiently read to the memory in a cache-free reading mode, and the video data is output to the display equipment.

A method of decoding compressed video or image data, or encoding uncompressed video or image data, using an 8-bit primary transform kernel matrix. The method can include determining whether to use a first transform kernel matrix having a first size type or a second transform kernel matrix having a second size type that is smaller than the first size type. When the result of the determination is to use the first transform kernel matrix, an 8-bit primary transform kernel matrix having a size of 64 points or more is used to encode or decode a target file. Otherwise, the method can include extracting the second transform kernel matrix of the second size type from the first transform matrix, and using the extracted second transform kernel matrix to encode or decode the target file.

A system processing hardware executes a video compression encoder based on a machine learning (ML) model to receive uncompressed video content and corresponding motion compensated video content, compare the uncompressed and motion compensated video content to identify an image space residual, transform the image space residual into a latent space representation of the uncompressed video content, and transform the motion compensated video content into a latent space representation of the motion compensated video content using a trained image compression ML model. The video compression encoder based on the ML model further encodes the latent space representation of the image space residual to produce an encoded latent residual, encodes the latent space representation of the motion compensated video content using the trained image compression ML model to produce an encoded latent video content, and produces compressed video content corresponding to the uncompressed video content using the encoded latent residual and the encoded latent video content.

A system includes: a machine learning (ML) model-based video encoder configured to: receive an uncompressed video sequence including a plurality of video frames; determine a first subset of video frames and a second subset of video frames from the plurality of video frames; encode the first subset of video frames to produce a first subset of compressed video frames; and identify first decompression data for the first subset of compressed video frames. The ML model-based video encoder is further configured to: encode the second subset of video frames to produce a second subset of compressed video frames; and identify second decompression data for the second subset of compressed video frames. The first decompression data is dedicated to decoding the first subset of compressed video frames and not dedicated to the second subset of compressed video frames, and the second decompression data is dedicated to decoding the second subset of compressed video frames and not dedicated to the first subset of compressed video frames.

A media router for down-sampled video routing is provided. The media router includes input cards, cross-point cards and output cards. The input cards include input terminals to receive high bandwidth video signals, a compression module to compress the high bandwidth video signals to generate low bandwidth video signals, and output terminals to transmit the low bandwidth video signals. The cross-point cards include input ports to receive the low bandwidth video signals generated by the input cards, output ports to transmit the low bandwidth video signals, and a cross-point switch for routing the low bandwidth video signals between the input ports and a designated output port. The output cards include input ports to receive the compressed video signal from a cross-point card, a decompression module for converting the compressed video signal into an uncompressed video signal, and output ports for transmitting the uncompressed video signal.

A system includes a machine learning (ML) model-based video encoder configured to receive an uncompressed video sequence including multiple video frames, determine, from among the multiple video frames, a first video frame subset and a second video frame subset, encode the first video frame subset to produce a first compressed video frame subset, and identify a first decompression data for the first compressed video frame subset. The ML model-based video encoder is further configured to encode the second video frame subset to produce a second compressed video frame subset, and identify a second decompression data for the second compressed video frame subset. The first decompression data is specific to decoding the first compressed video frame subset but not the second compressed video frame subset, and the second decompression data is specific to decoding the second compressed video frame subset but not the first compressed video frame subset.

The present invention concerns a method of encapsulating one or more uncompressed images in a media file, wherein the method comprises: generating generic description information describing the images and indicating that the images are uncompressed; and encapsulating the generic description information and the one or more images in the media file, the uncompressed images being encapsulated as one or more image items, the generic description information being encapsulated in a metadata part of the media file describing the image items.

An embodiment includes generating, using a first set of parameters, a first quantization matrix. The first set of parameters includes a set of coefficients of a formula that generates quantization values in the first quantization matrix. An embodiment includes encoding, using the first quantization matrix, a frame in an uncompressed video stream, the encoding generating a compressed video stream corresponding to the uncompressed video stream.

A method for analysing the change between two frames of an uncompressed video stream, including estimating a shift and statistical deviation between all or part of the frames in at least one direction, and using the one or more computed shifts and statistical deviations to detect a significant change between the frames. A method for coding an uncompressed video stream, wherein information relating to a significant change is used to modify the uncompressed video stream or coding parameters of the uncompressed video stream, in order to avoid peaks in rate. A device implementing either of the methods, to a complete transmitting system, to a computer program product comprising program instructions allowing the steps of the methods to be executed, and to a recording medium comprising the computer program product.