Intel Corporation

Sony Corporation

Sony Electronics Inc

Hfi Innovation Inc.

Mitsubishi Electric Research Laboratories, Inc.

Mediatek Singapore Pte. Ltd.

Tahoe Research, Ltd.

Mitsubishi Electric Research Labs.

Ximin Zhang

Sang-hee Lee

Ming-Chang Liu

Keith W Rowe

Sang-Hee Lee

Shan Liu

Yi-Jen Chiu

Changliang Wang

Dmitry E Ryzhov

Abhishek R Appu

Joydeep Ray

Anthony Vetro

Huifang Sun

Stanley J Baran

Jong Dae Oh

Atthar H Mohammed

Hiu-Fai R Chan

Zhijun Lei

Jill MacDonald Boyce

Yunbiao Lin

Shaw-Min Lei

Scott R Janus

James M Holland

Bin Zhu

Ya-Ti Peng

Yun-Qing Shi

Liangyin Yu

Hung-Ju Lee

Mark Allen Robertson

Gazi Ali

Chen Wang

Sebastian Possos

Hao-Song Kong

Zhan Lou

Sri Ranjan Srikantam

Huan Dou

Qing Li

Fan He

Szu-Wei Lee

Yu-Ting Li

Ming Chang Liu

Methods, apparatus, systems, and articles of manufacture are disclosed to improve video encoding. An example apparatus includes at least one memory, instructions, and processor circuitry to at least one of execute or instantiate the instructions to identify a first video frame as a critical video frame with respect to a previous video frame, generate filter coefficients of a video filter based on pixel data of the first video frame or pixel data of a previous critical video frame, and encode the first video frame with the filter coefficients.

Apparatus, articles of manufacture, and methods for improved adaptive loop filtering in video encoding

An example apparatus for encoding video frames includes a mask selector to select a subset of visual masks according to an actual target compression ratio and GOP configuration and a complexity estimator to estimate a picture level spatial/temporal complexity for a current frame. The example apparatus further includes a GOP adaptive visual mask selector to specify a visual mask from the subset of the visual masks corresponding to the estimated spatial and temporal complexity value a good enough picture QP deriver to derive a good enough picture QP value using the visual mask. The example apparatus also includes an adjustor to adjust the good enough picture QP value based on block level human visual system sensitivity and statistics of already encoded frames to obtain a final human visual system QP map.

Human visual system adaptive video coding

Methods, apparatus, systems, and articles of manufacture are disclosed to improve video encoding. An example apparatus includes at least one memory, instructions, and processor circuitry to generate a pool of clipping index set candidates by executing a machine learning model, select a clipping index set from the pool of the clipping index set candidates based on a rate distortion cost associated with the clipping index set, the clipping index set including clipping coefficients, and filter a video frame based on the clipping coefficients.

Apparatus, articles of manufacture, and methods for clipping index identification in adaptive loop filtering for video encoding

Techniques related to adaptive quantization matrix selection using machine learning for video coding are discussed. Such techniques include applying a machine learning model to generate an estimated quantization parameter for a frame and selecting a set of quantization matrices for encode of the frame from a number of sets of quantization matrices based on the estimated quantization parameter.

Determining adaptive quantization matrices using machine learning for video coding

An example apparatus for adaptively encoding video frames includes a network analyzer to predict an instant bitrate based on channel throughput feedback received from a network. The apparatus also includes a content analyzer to generate ladder info based on a received frame. The apparatus further includes an adaptive decision executer to determine a frame rate, a video resolution, and a target frame size based on the predicted instant bitrate and the ladder outputs. The apparatus further includes an encoder to encode the frame based on the frame rate, the video resolution, and the target frame size.

Adaptively encoding video frames using content and network analysis

Techniques related to video coding include content adaptive quantization that provides a selection between objective quality and subjective quality delta QP offsets.

Method and system of video coding with content adaptive quantization

Techniques related to adaptive quality boosting for low latency video coding are discussed. Such techniques include segmenting each of a number of temporally adjacent video frames into unique high encode quality regions and encoding each of the video frames by applying a coding quality boost to the high encode quality regions relative to other regions of the video frames.

Adaptive quality boosting for low latency video coding

Techniques related to quantization parameter estimation for coding intra and scene change frames are discussed. Such techniques include generating features based on an intra or scene change frame including a proportion of smooth blocks and one or both of a measure of block variance and a prediction distortion, and applying a machine learning model to generate an estimated quantization parameter for encoding the intra or scene change frame.

Video encoding rate control for intra and scene change frames using machine learning

An embodiment may include a display processor, memory to store a 2D frame corresponding to a projection from a 360 video, and a quality selector to select a quality factor for a block of the 2D frame based on quality information from neighboring blocks of the 2D frame, including blocks which are neighboring only in the 360 video space. The system may also include a range adjuster to adjust a search range for the 2D frame based on a search area of the 2D frame, a viewport manager to determine if a request for a viewport of the 2D frame extends beyond a first edge of the 2D frame and to fill the requested viewport with wrap-around image information, and/or a motion estimator to estimate motion information based on both color information and depth information. Other embodiments are disclosed and claimed.

360 neighbor-based quality selector, range adjuster, viewport manager, and motion estimator for graphics

Techniques related to accelerated video enhancement using deep learning selectively applied based on video codec information are discussed. Such techniques include applying a deep learning video enhancement network selectively to decoded non-skip blocks that are in low quantization parameter frames, bypassing the deep learning network for decoded skip blocks in low quantization parameter frames, and applying non-deep learning video enhancement to high quantization parameter frames.

Video codec assisted real-time video enhancement using deep learning

Methods, apparatus, systems, and articles of manufacture are disclosed. Example apparatus disclosed herein are to process features extracted from first and second downscaled image frames to determine block classifications for respective blocks of the first and the second downscaled image frames. Disclosed example apparatus are also to generate a map based on the block classifications, the map including values representative of amounts of change associated with blocks of the second downscaled image frame and corresponding blocks of the first downscaled image frame. Disclosed example apparatus are further to adjust a quantization parameter of a full-scale image frame based on the map, the full-scale image frame corresponding to the at least one of the first or the second downscaled image frames.

Methods and apparatus to encode video with region of motion detection

Techniques related to coding video using adaptive quantization rounding offsets for use in transform coefficient quantization are discussed. Such techniques may include determining the value of a quantization rounding offset for a picture of a video sequence based on evaluating a maximum coding bit limit of the picture, a quantization parameter of the picture, and parameters corresponding to the video.

Content adaptive quantization for video coding

An example apparatus for encoding video frames includes a frame analyzer to execute a look ahead analysis on a received set of frames. The frame analyzer includes a look ahead video analyzer to down-sample the set of frames and encode the down-sampled frames with a constant quantization parameter (QP). A number of generated bits for each of the frames is used as a complexity measurement for each of the frames. The apparatus further includes an encoder to encode the input frame using an adjusted bit allocation based on a relative complexity of the input frame in relation to a complexity of the set of frames.

Adaptively encoding video frames based on complexity

Embodiments are generally directed to transport controlled video coding. An embodiment of an apparatus includes one or more processors to process data; a memory to store data, including data for video streaming; and a video processing mechanism including an encoder and a transport mechanism, wherein the video processing mechanism is to generate a prediction of channel throughput for a network channel, encode one or more bitstreams based on the prediction, including encoding a plurality of bitstreams including a first bitstream and a second bitstream if the prediction indicates an increase or decrease in channel throughput and encoding a single bitstream if the prediction indicates a stable channel throughput; and select a bitstream of the one or more bitstreams for a current frame.

Transport controlled video coding

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San Jose, CA, United States of America

Ximin Zhang