Huawei Technologies Co., Limited

Ittiam Systems (p) Ltd.

Amazon Technologies, Inc.

Sriram Sethuraman

Jeeva Raj A

Sagar Kotecha

Semih Esenlik

Murali Babu Muthukrishnan

Yongjun Wu

Hai Wei

Arvind Raman

Sankaranarayanan Parameswaran

Dileep Kumar Tamia

Rahul Vanam

Deepthi Nandakumar

Manish Singhal

Bhavani Gopalakrishna Rao

Sarat Chandra Vadapalli

Vishal Jain

Sandesh Ghanta

Zaixi Shang

Shan Liu

Haitao Yang

Huanbang Chen

Jiantong Zhou

Shan Gao

Xiang Ma

Zongyi Liu

Aditya P Kulkarni

Kumar Rahul

Xiaohan Nie

Preethi Konda

Sattam Dasgupta

Anil Kumar Agara Venkatesha Rao

Jay Nitin Shingala

Arun Kumar Patil

Satheesh Sadanand

Anirudh Seth

Tharun Battula

Chirag Jain

Shantanu Bhaduri

Joshua Peter Ebenezer

Santhoshkumar Chilkundasrinivasamurthy

Biswadeep Sengupta

Ashwini Kumar

Durga Venkata Narayanababu Laveti

Malavika Bhaskaranand

Nithya Viswanathan Shyla

Omkiran Sharma

Dinesh Kumar

Mini Jain

Ambudhar Tripathi

Yixu Chen

Given that decoder side motion vector refinement/derivation is a normative aspect of a coding system, the encoder will also have to perform the same error surface technique in order to not have any drift between the encoder's reconstruction and the decoder's reconstruction. Hence, all aspects of all embodiments are applicable to both encoding and decoding systems. In template matching, the refinement movement occurs only in the reference starting from the sub-pixel accurate center that is derived based on the explicitly signaled merge index or implicitly through cost evaluations. In bilateral matching (with or without averaged template), the refinements start in the reference lists L0 and L1 starting from the respective sub-pixel accurate centers that are derived based on the explicitly signaled merge index or implicitly through cost evaluations.

Error surface based sub-pixel accurate refinement method for decoder side motion vector refinement

Techniques are described for training and use of machine learning models to determine objective video quality scores. Video quality scores predict the quality of video content perceived by viewers. Quality scores have various uses, including the selection of encoding profiles and determination of encoding ladders. A core model and residual model may be used to determine quality scores.

Objective video quality assessment models based on bitstream, and additional pixel domain features

Disclosed are various embodiments for detecting potential quality issues in encoded video content. Frame level metrics included in metric data that is associated with an encoded video can be analyzed and one or more quality scores can be calculated using the frame level metric values. If the quality scores meet or exceed one or more threshold values, an alarm notification can be generated that identifies a video segment that has a quality issue along with the one or more quality scores. The alarm notification can be sent to an entity for further evaluation of the encoded video.

Detecting quality issues in encoded video

A bidirectional optical flowing prediction method includes obtaining an initial motion vector pair for a current block, obtaining a forward and a backward prediction block according to the forward motion vector and a backward prediction block according to the initial motion vector pair, and calculating gradient parameters for a current sample in the current block. The method further includes obtaining at least two sample optical flow parameters, including a first parameter and a second parameter, for the current sample based on the gradient parameters, obtaining block optical flow parameters based on sample optical flow parameters of samples in the current block, and obtaining a prediction value of the current block. One of the block optical flow parameters is obtained by multiplying the first parameter and a sign function of the second parameter, and the sign function is a piecewise function with at least three subintervals.

Encoder, a decoder and corresponding methods for inter prediction using gradient parameters

A method for inter-prediction of a current image block in a current picture of a video is provided. The method includes determining whether a first temporal distance (such as TD0) is equal to a second temporal distance (such as TD1), wherein the first temporal distance is represented in terms of a difference between a picture order count value of the current picture and a picture order count value of a first reference picture; and the second temporal distance is represented in terms of a difference between a picture order count value of a second reference picture and the picture order count value of the current picture; and performing no motion vector refinement (DMVR) procedure when it is determined that the first temporal distance (TD0) is not equal to the second temporal distance (TD1). Thus the DMVR procedure is restricted to only the image block with equal-distance references.

Apparatus and method for conditional decoder-side motion vector refinement in video coding

A method for determining a motion vector to be used in inter-prediction of a current block of a video picture, the method comprising: obtaining an initial motion vector; obtaining at least two groups of points according to the initial motion vector, wherein a first group of the at least two groups of points only includes a first point corresponding to the initial motion vector and additional N points with N≥0 and wherein each point of the first group is closer to the first point than any point of the points of a second group of the at least two groups; and obtaining the motion vector for the current block according to the at least two groups of points and a cost function.

Search region for motion vector refinement

In some embodiments, a video encoder or a video decoder obtains a pair of motion vectors for a current coding block of a video signal with respect to two reference frames. Predictions of the current block are generated using the pair of motion vectors from respective reference frames. The video encoder or video decoder further determine an optical flow for the current coding block based on samples values in the predictions. One component of the optical flow is determined based on the other component of the optical flow. Bi-predictive optical flow (BPOF) can be performed on the current coding block using the determined optical flow.

Optical flow based video inter prediction

It is provided a method of video coding implemented in a decoding device or an encoding device, the method comprising: obtaining initial motion vectors for a current block; obtaining first predictions for a sample value in the current block based on the initial motion vectors; calculating a first matching cost according to the first predictions; determining whether an optical flow refinement process should be performed or not, according to at least one preset condition, the at least one preset condition comprising a condition of whether the calculated first matching cost is equal to or larger than a threshold value; and performing an optical flow refinement process for obtaining a final inter prediction for the sample value in the current block, when it is determined that the optical flow refinement process should be performed.

Early termination for optical flow refinement

Techniques are described for pre-processing frames to apply a temporal filter. A P-frame may be temporally filtered based on neighboring reference frames. The temporal filter may be applied to blocks of the P-frame to improve alignment with dependent frames that are encoded based on blocks of the P-frame.

Video pre-processing using encoder-aware motion compensated residual reduction

A method for bi-directional optical flow prediction based on reference sample values from sample positions belonging to an extended region of a predicted block used in the bi-directional optical flow prediction of a current block is provided, wherein sub-pixel positions in the extended region of the predicted block in the reference picture are rounded to obtain integer pixel sample positions in the reference picture, wherein the reference sample values at the obtained integer pixel sample positions in the reference picture are used in the computation of boundary sample gradients corresponding to the boundaries of the predicted block in the reference picture.

Device and method for computing position of integer grid reference sample for block level boundary sample gradient computation

Techniques are described for facilitating client-side augmented reality overlay of secondary content during live events. Regions for overlaying secondary content are identified along with attributes for each region. A client device may then used the attributes to overlay secondary content in each region prior to playback.

Client side augmented reality overlay

The present disclosure provides an inter prediction method, comprising the steps of obtaining an initial motion vector and a reference picture for bi-prediction; obtaining sets of candidate sample positions in the reference picture according to the initial motion vector and candidate motion vectors, wherein each candidate motion vector is derived by the initial motion vector and a respective motion vector offset, and wherein each set of candidate sample positions corresponds to each candidate motion vector; obtaining a respective set of sample positions from each set of candidate sample positions; computing a matching cost for each candidate motion vector within each set of sample positions; obtaining a refined motion vector based on the computed matching cost of each candidate motion vector; and obtaining prediction values for a current block based on the refined motion vector.

DMVR using decimated prediction block

Encoder, a decoder and corresponding methods for inter prediction using bidirectional optical flow

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Bengaluru, India

Sriram Sethuraman