Alibaba Group Holding Limited

Minghai Qin

Tae Meon Bae

Yen-kuang Chen

Guanlin Wu

Sicheng Li

Yen-Kuang Chen

Shaolin Xie

Fei Sun

Qinggang Zhou

Yuhao Wang

Kai Xu

Yuanwei Fang

Yuan Xie

Chunsheng Liu

Zhibin Xiao

Zihao Liu

Jian Lou

Lingjie Xu

Xiaoxin Fan

Ping Chen

Xinyang Yu

Zhenzhen Wang

A method for of encoding an application screen comprises partitioning graphic data into a plurality of graphic layers and classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. The method further comprises classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. Further, the method comprises rendering and encoding the one or more SC layers using a first codec and the one or more non-SC layers using a second codec.

Method and apparatus for efficient application screen compression

Scene aware video content encoding techniques can determine if video content is a given content type and is one of one or more given titles that include one or more given scenes. The one or more given scenes of the video content of the given type and given one of the titles can be encoded using corresponding scenes specific encoding parameter values, and the non-given scenes can be encoded using one or more general encoding parameter values. The one or more given titles can be selected based on a rate of streaming of various video content titles of the given type.

Scene aware video content encoding

Transcoding bitrate prediction techniques can include receiving a first encoded content. A transcoder bitrate can be estimated based on regression over a video quality estimator of the first encoded content and a second encoded content. The estimated transcoder bitrate can be utilized to transcoding the first encoded content into the second encoded.

Transcoder target bitrate prediction techniques

The present disclosure relates to a method for compensating an image. The method comprises estimating transform coefficients of a frequency component for a first image based on the first image, performing a dot multiplication operation between the estimated transform coefficients and a basis function associated with the frequency component to generate a compensation image, and combining the first image and the compensation image to generate a combined image.

Frequency specific compression and compensation technique in image processing

The present disclosure relates to a method and an apparatus for representation of a sparse matrix in a neural network. In some embodiments, an exemplary operation unit includes a buffer for storing a representation of a sparse matrix in a neural network, a sparse engine communicatively coupled with the buffer, and a processing array communicatively coupled with the sparse engine. The sparse engine includes circuitry to: read the representation of the sparse matrix from the buffer, the representation comprising a first level bitmap, a second level bitmap, and an element array; decompress the first level bitmap to determine whether a block of the sparse matrix comprises a non-zero element; and in response to the block comprising a non-zero element, decompress the second level bitmap using the element array to obtain the block of the sparse matrix. The processing array includes circuitry to execute the neural network with the sparse matrix.

Apparatus and method for representation of a sparse matrix in a neural network

The present disclosure relates to a computer-implemented method for processing video data. The method comprises receiving a user input corresponding to a first picture of the video data, generating, based on the user input, prediction information of the first picture with respect a reference picture of the video data, and encoding the first picture using the prediction information.

Video encoding technique utilizing user guided information in cloud environment

Methods and systems are provided for implementing preprocessing operations and augmentation operations upon image datasets transformed to frequency domain representations, including decoding images of an image dataset to generate a frequency domain representation of the image dataset; performing a resizing operation based on resizing factors on the image dataset in a frequency domain representation; performing a reshaping operation based on reshaping factors on the image dataset in a frequency domain representation; and performing a cropping operation on the image dataset in a frequency domain representation. The methods and systems may further include performing an augmentation operation on the image dataset in a frequency domain representation. Methods and systems of the present disclosure may free learning models from computational overhead caused by transforming image datasets into frequency domain representations. Furthermore, computational overhead caused by inverse transformation operations is also alleviated.

Data preprocessing and data augmentation in frequency domain

A neural network, trained on a plurality of random size data samples, can receive a plurality of inference data samples including samples of different sizes. The neural network can generate feature maps of the plurality of inference data samples. Pooling can be utilized to generate feature maps having a fixed size. The fixed size feature maps can be utilized to generate an indication of a class for each of the plurality of inference data samples.

Variable input size techniques for neural networks

Vector-vector multiplication or matrix-matrix multiplication computation on computing systems can include computing a first portion of a vector-vector multiplication product based on a most-significant-bit set of a first vector and a most-significant-bit set of a second vector, and determining if the first portion of the vector-vector multiplication product is less than a threshold. If the first partial vector-vector multiplication product is not less than the threshold, a remaining portion of the vector-vector multiplication product can be computed, and a rectified linear vector-vector multiplication product can be determined for the sum of the first portion of the vector-vector multiplication product and the remaining portion of the vector-vector multiplication product. If the first portion of the vector-vector multiplication product is less than the threshold, computation of the remaining portion of the vector-vector multiplication product can be skipped and the rectified linear vector-vector multiplication product can be set to a zero scalar.

Vector-vector multiplication techniques for processing systems

Methods and apparatuses for video transcoding based on spatial or temporal importance include: in response to receiving an encoded video bitstream, decoding a picture from the encoded video bitstream; determining a first level of spatial importance for a first region of a background of the picture based on an image segmentation technique; applying to the first region a first resolution-enhancement technique associated with the first level of spatial importance for increasing resolution of the first region by a scaling factor, wherein the first resolution-enhancement technique is selected from a set of resolution-enhancement techniques having different computational complexity levels; and encoding the first region using a video coding standard.

Method and system for video transcoding based on spatial or temporal importance

The present disclosure relates to a method and an apparatus for map reduce. In some embodiments, an exemplary processing unit includes: a 2-dimensional (2D) processing element (PE) array comprising a plurality of PEs, each PE comprising a first input and a second input, the first inputs of the PEs in a linear array in a first dimension of the PE array being connected in series and the second inputs of the PEs in a linear array in a second dimension of the PE array being connected in parallel, each PE being configured to perform an operation on data from the first input or second input; and a plurality of reduce tree units, each reduce tree unit being coupled with the PEs in a linear array in the first dimension or the second dimension of the PE array and configured to perform a first reduction operation.

Apparatuses and methods for map reduce

The present disclosure provides methods and systems for executing a neural network. The method includes: receiving a plurality of input vectors of input data; generating, among the plurality of input vectors, an estimation value associated with a subset of an input vector based on a weight vector of the activation function; determining whether the estimation value associated with the subset of the input vector satisfies a threshold condition; and determining an output of the activation function based on the estimation value.

Method and system for executing neural network

Embodiments of the disclosure provide systems and methods for processing video content. The method can include: receiving raw video data of a video; determining a texture complexity for the video based on the raw video data; determining an encoding mode for the raw video data based on the texture complexity; and encoding the raw video data using the determined encoding mode.

Method and system for processing video content

Methods and systems are provided for implementing static channel filtering operations upon image datasets transformed to frequency domain representations, including decoding images of an image dataset to generate a frequency domain representation of the image dataset; discarding coefficient values of one or more particular frequency channels of each image of the image dataset in a frequency domain representation; and transporting the image dataset in a frequency domain representation to one or more special-purpose processor(s). Methods and systems of the present disclosure may enable a filtered image dataset to be input to a second layer of a learning model, bypassing a first layer, or may enable a learning model to be designed with a reduced-size first layer. This may achieve benefits such as reducing computational overhead and time of machine learning training and inference computations, reducing volume of image data input into the learning model, and reducing convergence time.

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Hangzhou, China

Minghai Qin