Described herein are video streaming techniques for applications and workloads executed in the cloud. In one example, the cloud server device encodes display frames using low-delay encoding techniques for transmission to a client device. The cloud server device receives an overlay bitstream from a client device, combines the overlay data with the display frames, and encodes the frames for the viewers using statistics from the display frames encoded for the client device and/or from the overlay data. The cloud server device can then transmit the bitstream to a third device for viewing (e.g., to a viewer device or a streaming server device).

The present technology makes it easy to present an image having appropriate image quality at a receiver side that receives high-frame-rate moving image data. A video stream obtained by encoding moving image data having a high frame rate is generated. A container containing the video stream is transmitted. Blur control information for controlling blur is inserted into a layer of the container and/or a layer of the video stream. The blur control information gives, for example, weighting coefficients for individual frames in a blurring process for adding image data of neighboring frames to image data of a current frame.

The present technology makes it easy to present an image having appropriate image quality at a receiver side that receives high-frame-rate moving image data. A video stream obtained by encoding moving image data having a high frame rate is generated. A container containing the video stream is transmitted. Blur control information for controlling blur is inserted into a layer of the container and/or a layer of the video stream. The blur control information gives, for example, weighting coefficients for individual frames in a blurring process for adding image data of neighboring frames to image data of a current frame.

Techniques are described for compressing and decompressing data using machine learning systems. An example process can include receiving a plurality of images for compression by a neural network compression system. The process can include determining, based on a first image from the plurality of images, a first plurality of weight values associated with a first model of the neural network compression system. The process can include generating a first bitstream comprising a compressed version of the first plurality of weight values. The process can include outputting the first bitstream for transmission to a receiver.

The present disclosure refers to methods and apparatuses for processing of high-resolution video content. In an embodiment, a method includes generating a first group of video frames from the video content. The first group of video frames has a first resolution lower than a resolution of the video content and a first rate-distortion score. The method further includes generating a second group of video frames from the video content. The second group of video frames has a second resolution lower than the resolution of the video content and a second rate-distortion score. The method further includes selecting an optimal group of video frames from the first and second groups of video frames based on a comparison between the first and second rate-distortion scores. The optimal group of video frames has a rate-distortion score lower than the first and the second rate-distortion scores.

An image processing apparatus for performing correction for each frame group including a predetermined number of frames into which video data is divided includes a decoding unit configured to obtain a corrected frame group by correcting a second frame group, which is a frame group continuous with a first frame group in time, using a feature quantity of the first frame group. The decoding unit performs the correction so that subjective image quality based on a relationship between the second frame group and a frame group subsequent to the second frame group in time is increased and so that a predetermined classifier classifies that a frame group in which the second frame group is concatenated with the frame group subsequent to the second frame group in time is the same as a frame group in which the corrected frame group is concatenated with a corrected frame group obtained by correcting the frame group subsequent to the second frame group in time.

At least a method and an apparatus are presented for efficiently encoding or decoding video. For example, a plurality of frames is provided to a motion estimator to produce an output comprising estimated motion information. The estimated motion information is provided to an auto-encoder or an auto-decoder to produce an output comprising reconstructed motion field. The reconstructed motion field and one or more decoded frames of the plurality of frames are provided to a deep neural network to produce an output comprising refined bi-directional motion field. The video is encoded or decoded based on the refined bi-directional motion field.

At least a method and an apparatus are presented for efficiently encoding or decoding video. For example, a plurality of frames is provided to a motion estimator to produce an output comprising estimated motion information. The estimated motion information is provided to an auto-encoder or an auto-decoder to produce an output comprising reconstructed motion field. The reconstructed motion field and one or more decoded frames of the plurality of frames are provided to a deep neural network to produce an output comprising refined bi-directional motion field. The video is encoded or decoded based on the refined bi-directional motion field.

A method includes establishing communication between a first user device and a second user device using a first codec and filtering an input signal indicating an estimated unfiltered available bandwidth for the communications by applying a current filter including one of a first filter when the estimated unfiltered available bandwidth is less than a first threshold value or greater than a second threshold value or a second filter when the estimated unfiltered available bandwidth is between and including the first and second threshold values. The method includes adaptively switching the current filter as a function of the filtered input signal and the first and second threshold values. When the filtered input signal satisfies a channel bandwidth threshold for at least a predetermined period of time, the method includes switching from using the first codec to using a second codec for the communication between the first and second user devices.

A device, and method of signing a video segment comprising one or more groups of pictures, GOPs, wherein each GOP comprises a header and one or more frames, are disclosed. For each of the one or more GOPs a GOP hash is produced and the GOP hash is digitally signed by means of a digital signature to produce a signed GOP hash. For each GOP except a last GOP of the one or more GOPs the respective signed GOP hash is saved in the header of a subsequent GOP. An additional GOP is added to the video segment after the last GOP of the one or more GOPs, wherein the additional GOP comprising a header and one or more frames. The signed GOP hash of the last GOP of the one or more GOPs is saved in the header of the additional GOP.