Intel Corporation

Tahoe Research, Ltd.

Yi-Jen Chiu

Lidong Xu

Wenhao Zhang

Yu Han

Hong Jiang

Zhipin Deng

Xiaoxia Cai

Ya-Ti Peng

Jong Dae Oh

Ke Chen

Chen Wang

Stanley J Baran

Jill MacDonald Boyce

Jason Daniel Tanner

Sang-Hee Lee

Narayan Biswal

Jianguo Li

Barnan Das

Atthar H Mohammed

Sumit Mohan

Vijay Sundaram

Pieter Kapsenberg

Richmond Hicks

Louis A Lippincott

Archie Sharma

Prasenjit Biswas

Huan Dou

Mei-Chen Yeh

Hyeong-Seok Victor Ha

Ximin Zhang

Gokcen Cilingir

Sang-hee Lee

Mayuresh M Varerkar

Srikanth Potluri

Ping Liu

Wen-Fu Kao

Abhishek R Appu

Joydeep Ray

Prasoonkumar Surti

Qiang Li

Tae-Hoon Kim

Michael Apodaca

Atsuo Kuwahara

Changliang Wang

Dukhwan Kim

Yangzhou Du

Nilesh V Shah

Jorge E Caviedes

Hiu-Fai R Chan

Robert J Johnston

Satyanarayana Avadhanam

Iole Moccagatta

Jingyang Xue

Teahyung Lee

Atul Divekar

Bongjin Jun

Yu-Ting Li

Bin Wang

Lin Xu

Scott R Janus

Qian Xu

Yurong Chen

James M Holland

Hugues Labbe

Supratim Pal

Ning Lu

Keith W Rowe

Munsi Haque

Niraj Gupta

Prasanna Singamsetty

Jason Ross

Heon-Mo Koo

Yi-Chu Wang

Mahesh B Chappalli

Yingta Yeh

Maruthi Sandeep Maddipatla

Methods, articles, and systems of video coding use efficient intra block copying.

Method and system of video coding with efficient intra block copying

An apparatus for super resolution imaging includes a convolutional neural network () to receive a low resolution frame () and generate a high resolution illuminance component frame. The apparatus also includes a hardware scaler () to receive the low resolution frame () and generate a second high resolution chrominance component frame. The apparatus further includes a combiner () to combine the high resolution illuminance component frame and the high resolution chrominance component frame to generate a high resolution frame ().

Super resolution using convolutional neural network

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

A method for image content classification is described herein. The method includes counting a number of distinct color numbers in an image. The method also includes clustering blocks with a same distinct color number into a same class and determining a block occupancy rate of each color number for the image. Finally, the method includes classifying the image according to the block occupancy rate via a plurality of classifiers communicatively coupled in series.

Image content classification

Systems, apparatus, articles, and methods are described including operations for size based transform unit context derivation. In an example encoder, first circuitry is to encode video input data into a bitstream according to a bitstream syntax, wherein the video input data includes one or more pictures, the one or more pictures are partitioned into one or more coding tree blocks, the one or more coding tree blocks are partitioned into slices including one or more coding tree blocks, the one or more coding tree blocks include one or more transform blocks according to a transform tree including a split_transform_flag indicative of the split of a given coding block into corresponding one or more transform blocks, the split_transform_flag is coded using CABAC, and a context index associated with the CABAC coding of the split_transform_flag is based on a value. Second circuitry of the encoder is to output the bitstream.

Size based transform unit context derivation

Methods and systems to improve a visual perception of dark scenes in video. An example device includes one or more processors to receive a frame of video segmented into a plurality of sub-regions. A local luminance histogram is generated for each sub-region. A global luminance histogram is generated for the entire frame of video and a global tone mapping curve is generated based on the global luminance histogram. A tone mapping LUT is generated for each sub-region based on the global tone mapping curve and the corresponding local luminance histogram for the sub-region. The frame of video is then modified using the tone mapping LUTs generated for each sub-region and sent to an output device.

Brightness and contrast enhancement for video

Systems, apparatus, articles of manufacture and methods for face augmentation in video are disclosed. An example apparatus includes executable code to detect a face of a subject in the video, detect a gender of the subject based on the face, detect a skin tone of the subject based on the face, apply a first process to smooth skin on the face in the video, apply a second process to change the skin tone of the face, apply a third process to slim the face, apply a fourth process to adjust a size of eyes on the face, and apply a fifth process to remove an eye bag from the face. One or more of the first process, the second process, the third process, the fourth process, or the fifth process adjustable based on one or more of the gender or an age. The example apparatus also includes one or more processors to generate modified video with beauty effects, the beauty effects based on one or more of the first process, the second process, the third process, the fourth process, or the fifth process.

Face augmentation in video

An example apparatus for enhancing video includes a decoder to decode a received 360-degree projection format video bitstream to generate a decoded 360-degree projection format video. The apparatus also includes a viewport generator to generate a viewport from the decoded 360-degree projection format video. The apparatus further includes a convolutional neural network (CNN)-based filter to remove an artifact from the viewport to generate an enhanced image. The apparatus further includes a displayer to send the enhanced image to a display.

Enhancing 360-degree video using convolutional neural network (CNN)-based filter

An apparatus for content based anti-aliasing is described herein. The apparatus comprises a detector, corrector, and downscaler. The detector is to detect potential aliased content in an input image, wherein the potentially aliased content occurs at a downscaled version of the input image. The corrector is to apply a correction to a single component of the input image. A downscaler may downscale the corrected input image to an output image according to a scaling factor.

Content based anti-aliasing for image downscale

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

An apparatus for edge aware upscaling is described herein. The apparatus comprises a potential edge detector, a thin-edge detector, a one-directional edge detector, a correlation detector, and a corrector. The potential edge detector identifies potential edge pixels in an input image, and the thin-edge detector detects thin edges in the potential edge pixels of the input image. The one-directional edge detector detects one-directional edges in the potential edge pixels of the input image, and the correlation detector detects strongly correlated edges in the potential edge pixels of the input image. The corrector derives a target output value based on an edge type and classification of a corresponding input pixel as identified by a source map point.

Edge-aware upscaling for improved screen content quality

Systems, apparatuses and methods may provide for technology to improve an appearance of objects that enter a viewable area of a 360-degree video. The technology may include a head mounted display (HMD), a viewport comprising a viewable area within the HMD, and a memory to store objects of interest not currently in the viewable area.

Object pre-encoding for 360-degree view for optimal quality and latency

Method and apparatus for deriving a motion vector at a video decoder. A block-based motion vector may be produced at the video decoder by utilizing motion estimation among available pixels relative to blocks in one or more reference frames. The available pixels could be, for example, spatially neighboring blocks in the sequential scan coding order of a current frame, blocks in a previously decoded frame, or blocks in a downsampled frame in a lower pyramid when layered coding has been used.

Methods and systems for motion vector derivation at a video decoder

A mechanism is described for facilitating adaptive resolution and viewpoint-prediction for immersive media in computing environments. An apparatus of embodiments, as described herein, includes one or more processors to receive viewing positions associated with a user with respect to a display, and analyze relevance of media contents based on the viewing positions, where the media content includes immersive videos of scenes captured by one or more cameras. The one or more processors are further to predict portions of the media contents as relevant portions based on the viewing positions and transmit the relevant portions to be rendered and displayed.

Adaptive resolution of point cloud and viewpoint prediction for video streaming in computing environments

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San Jose, CA, United States of America

Yi-Jen Chiu