Vid Scale, Inc.

Alibaba Group Holding Limited

Qualcomm Incorporated

Interdigital Vc Holdings, Inc.

Interdigital Madison Patent Holdings, Sas

Dolby Laboratories Licensing Corporation

Alibaba (China) Co., Ltd.

Alibaba Innovation Private Limited

Interdigital Patent Holdings, Inc.

Velos Media, LLC

Alibaba Damo (Hangzhou) Technology Co., Ltd.

Guangdong Oppo Mobile Telecommunications Corp., Ltd.

Alibaba Singapore Holding Private Limited

Alibaba Group Holding Limtted

Atibaba Group Holding Limited

Yan Ye

Yuwen He

Xiaoyu Xiu

Jiancong Luo

Ru-Ling Liao

Jie Chen

Yong He

Marta Karczewicz

Mohammed Golam Sarwer

Jie Dong

Peisong Chen

Zhao Wang

Philippe Hanhart

Ralph Neff

Ahmed Hamza

George W McClellan

Alexandros Michael Tourapis

Eduardo Asbun

Yiliang L Bao

Yan Zhang

Peng Yin

Louis Joseph Kerofsky

Giovanni Motta

Qian Chen

Shiqi Wang

Rahul Vanam

Guan-Ming Su

Walter Christian Gish

Sheng Qu

Chia-Yuan Teng

Ming-Chang Tsai

Ariela Zeira

Yuriy Reznik

Samir N Hulyalkar

Sharath Manjunath

Wei-Jung Chien

Eldad M Zeira

Liangping Ma

Anantharaman Balasubramanian

Athanasios Leontaris

Gregory S Sternberg

Hang Liu

Khaled Helmi El-Maleh

Xinwei Li

Eun Seok Ryu

Ali C Begen

Srinivas Gudumasu

Chia-Ming Tsai

Markus Kunstner

Can Bal

Saurav Bandyopadhyay

Chun-Chi Chen

Rahul P Panchal

Shurun Wang

Yufei Yuan

Zhifeng Chen

Xuan Huang

Bolin Chen

Eun Ryu

Peshala Vishvajith Pahalawatta

Arash Vosoughi

Gokce Dane

Byung Kwan Yi

Narendranath Malayath

Yen-Chi Lee

Hyukjune Chung

Naresh H Soni

Fanyi Duanmu

Jianhua Chen

Thorsten Laude

Yin Peng

Karl Ni

Shengyang Xu

Ru-ling Liao

Changyue Ma

Mohammad Golam Sarwer

Enming Luo

Maryam Azimi Hashemi

Jiun-Yu Kao

Yamei Chen

Nien-Chung Feng

Ruling Liao

Miaohui Wang

George W Mcclellan

Chen Peisong

He Yong

Fan Ling

Systems, methods, and instrumentalities may be provided for determining whether to bypass bi-directional optical flow (BDOF) if BDOF is used in combination with bi-prediction with coding unit (CU) weights (e.g., generalized bi-prediction (GBi)). A coding system may combine coding modes, coding techniques, and/or coding tools. The coding system may include a wireless transmit/receive unit (WTRU). For example, the coding system may combine BDOF and bi-prediction with CU weights (BCW). BDOF may include refining a motion vector associated with a current CU based at least in part on gradients associated with a location in the current CU. The coding system may determine that BDOF is enabled, and/or that bi-prediction with CU weights is enabled for the current CU. The coding system's determination that bi-prediction with CU weights is enabled and/or that BDOF is enabled may be based on one or more indications.

Bi-prediction for video coding

Processing video data may include capturing the video data with multiple cameras and stitching the video data together to obtain a 360-degree video. A frame-packed picture may be provided based on the captured and stitched video data. A current sample location may be identified in the frame-packed picture. Whether a neighboring sample location is located outside of a content boundary of the frame-packed picture may be determined. When the neighboring sample location is located outside of the content boundary, a padding sample location may be derived based on at least one circular characteristic of the 360-degree video content and the projection geometry. The 360-degree video content may be processed based on the padding sample location.

360-degree video coding using geometry projection

Systems, methods, and instrumentalities may be provided for discounting reconstructed samples and/or coding information from spatial neighbors across face discontinuities. Whether a current block is located at a face discontinuity may be determined. The face discontinuity may be a face boundary between two or more adjoining blocks that are not spherical neighbors. The coding availability of a neighboring block of the current block may be determined, e.g., based on whether the neighboring block is on the same side of the face discontinuity as the current block. For example, the neighboring block may be determined to be available for decoding the current block if it is on the same side of the face discontinuity as the current block, and unavailable if it is not on the same side of the face discontinuity. The neighboring block may be a spatial neighboring block or a temporal neighboring block.

Handling face discontinuities in 360-degree video coding

A method for receiving and displaying media content may be provided. The method may include requesting a set of DASH video segments that are associated with various viewports and qualities. The method may include displaying the DASH video segments. The method may include determining a latency metric based on a time difference between the display of a DASH video segment and one of: a device beginning to move, the device ceasing to move, the device determining that the device has begun to move, the device determining that the device has stopped moving, or the display of a different DASH video segment. The different DASH video segment may be associated with one or more of a different quality or a different viewport.

Metrics and messages to improve experience for 360-degree adaptive streaming

A VVC-standard encoder and a VVC-standard decoder are provided, implementing derivation of a BCW index according to cost values based on template matching. A template matching cost can be calculated for each among a set of possible BCW weight values, and a BCW weight value yielding a lowest template matching cost among each template matching cost calculated can be selected as a BCW index for a bi-predicted merge candidate. Alternatively, a template matching cost can be calculated for each among a subset of possible BCW weight values based on an inherited BCW weight. Additionally, a merge candidate BCW index can be derived while adjusting template matching cost of an inherited BCW weight from a value calculated according to the VVC standard and ECM specifications, or while adjusting template matching cost of a BCW weight having equal weight from a value calculated according to the VVC standard and ECM specifications.

Deriving bi-prediction with coding unit-level weight indices for merge candidates

The present disclosure provides systems and methods for processing video content. The method can include: receiving a bitstream comprising video content; determining whether a first signal associated with the video content satisfies a given condition; and in response to the determination that the first signal satisfies the given condition, disabling both a cross component adaptive loop filter (CCALF) process and a chroma adaptive loop filter (ALF) process.

Method and apparatus for processing video content with ALF and CCALF

Methods and apparatuses are provided for compressing video data based on keypoint features. An exemplary video compression method includes: receiving a video sequence; encoding one or more pictures of the video sequence; and generating a bitstream; wherein the encoding includes: representing a first picture by a first set of keypoints and a second set of keypoints, the second set comprising less keypoints than the first set; and compressing the video sequence based on the first set and second set of keypoints.

Keypoints based video compression

Methods and apparatuses are provided for processing video data. An exemplary method includes: decompressing a compressed frame to generate a key frame representing a face; generating, for the key frame, a first set of parameters associated with a 3-dimensional (3D) face representation of the face; reconstructing, for each of one or more inter frames, a second set of parameters associated with a 3D face representation of the face according to compressed inter-predicted residuals of the second set of parameters; and generating a video comprising the face based on the key frame, the first set of parameters, and the second set of parameters.

Method and apparatus for talking face video compression

Systems and methods are described for reducing the complexity of using bi-directional optical flow (BIO) in video coding. In some embodiments, bit-width reduction steps are introduced in the BIO motion refinement process to reduce the maximum bit-width used for BIO calculations. In some embodiments, simplified interpolation filters are used to generate predicted samples in an extended region around a current coding unit. In some embodiments, different interpolation filters are used for vertical versus horizontal interpolation. In some embodiments, BIO is disabled for coding units with small heights and/or for coding units that are predicted using a sub-block level inter prediction technique, such as advanced temporal motion vector prediction (ATMVP) or affine prediction.

Complexity reduction and bit-width control for bi-directional optical flow

A device may determine whether to enable or disable bi-directional optical flow (BIO) for a current coding unit (CU) (e.g., block and/or sub-block). Prediction information for the CU may be identified and may include prediction signals associated with a first reference block and a second reference block (e.g., or a first reference sub-block and a second reference sub-block). A prediction difference may be calculated and may be used to determine the similarity between the two prediction signals. The CU may be reconstructed based on the similarity. For example, whether to reconstruct the CU with BIO enabled or BIO disabled may be based on whether the two prediction signals are similar. It may be determined to enable BIO for the CU when the two prediction signals are determined to be dissimilar. For example, the CU may be reconstructed with BIO disabled when the two prediction signals are determined to be similar.

Motion-compensation prediction based on bi-directional optical flow

The present disclosure provides methods for convolutional-neural-network (CNN) based filter for video coding. An exemplary method includes: applying motion estimation to a target coding block, to determine a reference block of the target coding block; inputting, to a convolutional neural network (CNN) filter, image data associated with the target coding block and the reference block; and executing the CNN filter to determine a residual associated with the target coding block based on the input image data.

Convolutional-neutral-network based filter for video coding

Systems, methods, and instrumentalities are described herein for calculating local illumination compensation (LIC) parameters for bi-predicted coding unit (CU). The LIC parameters may be used to generate adjusted samples for the current CU and to address local illumination changes that may exist among temporal neighboring pictures. LIC parameters may be calculated based on bi-predicted reference template samples and template samples for a current CU. Bi-predicted reference template samples may be generated based on reference template samples neighboring temporal reference CUs. For example, the bi-predicted reference template samples may be generated based on averaging the reference template samples. The reference template samples may correspond to template samples for the current CU. A CU may be or may include a coding block and/or a sub-block that may be derived by dividing the coding block.

Motion compensated bi-prediction based on local illumination compensation

An input video or video stream may be obtained or received. The input video or video stream may include a plurality of video frames, and each frame may be divided into a plurality of blocks. A current block of the plurality of blocks may be predicted using a planar mode. Depending on which planar mode is used, different reference samples may be used for predicting a current sample in the current block.

On planar intra prediction mode

A coding device (e.g., that may be or may include encoder and/or decoder) may receive a frame-packed picture of 360-degree video. The coding device may identify a face in the frame-packed picture that the current block belongs to. The coding device may determine that a current block is located at a boundary of the face that the current block belongs to. The coding device may identify multiple spherical neighboring blocks of the current block. The coding device may identify a cross-face boundary neighboring block. The coding device may identify a block in the frame-packed picture that corresponds to the cross-face boundary neighboring block. The coding device may determine whether to use the identified block to code the current block based on availability of the identified block. The coding device may code the current block based on the determination to use the identified block.

360-degree video coding using face continuities

Methods for encoding a video sequence into a bitstream and decoding a bitstream to output one or more pictures for a video stream. An exemplary method includes: receiving a video sequence; encoding one or more pictures of the video sequence; and generating a bitstream associated with the encoded pictures, wherein the encoding comprises: predicting chroma samples within a current block based on luma samples corresponding to the chroma samples by a plurality of cross-component residual models (CCRMs).

Cross component prediction

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San Diego, CA, United States of America

Yan Ye