Qualcomm Incorporated

Ankitesh Kumar Singh

Marta Karczewicz

Muhammed Zeyd Coban

Hilmi Enes Egilmez

Cheng-Teh Hsieh

Techniques are described herein for processing video data using a neural network system. For instance, a process can include generating, by a first convolutional layer of an encoder sub-network of the neural network system, output values associated with a luminance channel of a frame. The process can include generating, by a second convolutional layer of the encoder sub-network, output values associated with at least one chrominance channel of the frame. The process can include generating, by a third convolutional layer based on the output values associated with the luminance channel of the frame and the output values associated with the at least one chrominance channel of the frame, a combined representation of the frame. The process can further include generating encoded video data based on the combined representation of the frame.

Front-end architecture for neural network based video coding

The present disclosure relates to methods and devices for data or graphics processing including an apparatus, e.g., a GPU. The apparatus may receive at least one bitstream including a plurality of bits, each of the bits corresponding to a position in the at least one bitstream, and each of the bits being associated with color data. The apparatus may also arrange an order of the plurality of bits in the at least one bitstream, such that at least one of the bits corresponds to an updated position in the at least one bitstream. Additionally, the apparatus may convert, upon arranging the order of the bits, the color data associated with each of the plurality of bits in the at least one bitstream. The apparatus may also compress, upon converting the color data associated with each of the bits, the plurality of bits in the at least one bitstream.

Methods and apparatus for lossless compression of GPU data

Systems and techniques are described herein for processing video data. In some aspects, a method can include obtain, by a machine learning system, input video data. The input video data includes one or more luminance components for a current frame. The method can include determining, by the machine learning system, motion information for the luminance component(s) of the current frame and motion information for one or more chrominance components of the current frame using the luminance component(s) for the current frame. In some cases, the method can include determining the motion information for the luminance component(s) based on the luma component(s) of the current frame and at least one reconstructed luma component of a previous frame. In some cases, the method can further include determining the motion information for the chrominance component(s) of the current frame using the motion information determined for the luminance component(s) of the current frame.

Machine learning based flow determination for video coding

Systems and techniques are described herein for processing video data using a neural network system. For instance, a process can include generating, by a first convolutional layer of an encoder sub-network of the neural network system, output values associated with a luminance channel of a frame. The process can include generating, by a second convolutional layer of the encoder sub-network, output values associated with at least one chrominance channel of the frame. The process can include generating a combined representation of the frame by combining the output values associated with the luminance channel of the frame and the output values associated with the at least one chrominance channel of the frame. The process can include generating encoded video data based on the combined representation of the frame.

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San Diego, CA, United States of America

Ankitesh Kumar Singh