Alibaba Group Holding Limited

Sicheng Li

Minghai Qin

Tae Meon Bae

Guanlin Wu

Yen-kuang Chen

Yen-Kuang Chen

Yuanwei Fang

Zihao Liu

Shaolin Xie

Qinggang Zhou

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

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 system and method for image processing. In some embodiments, an exemplary image processing method includes: receiving an image; compressing, with a compression neural network, the image into a compressed representation; and performing, with a processing neural network, a machine learning task on the compressed representation to generate a learning result. The compression neural network and the processing neural network are jointly trained.

System and method for compressing images for remote processing

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

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

A video processing unit can include a non-object-based region-of-interest detection neural network, a threshold selection module and a region-of-interest map generator. The non-object-based region-of-interest detection neural network can be configured to receive a video frame and generate a plurality of candidate non-object-based region-of-interest blocks. The threshold selection module can be configured to receive the plurality of candidate non-object-based region-of-interest blocks and identify a plurality of selected region-of-interest blocks based on a predetermined threshold. The region-of-interest map generator can be configured to receive the selected non-object-based region-of-interest blocks and generate a region-of-interest map.

Region-of-interest based video encoding

Video coding techniques including differential bit rate or quality coding of one or more regions of interest and one or more non-regions of interest based on information including one or more of coordinates of the one or more regions of interest, a target complexity, residual encoder bit data, a requested quality, a difference between the current video data frame and a reconstructed video data frame, a target quality, a requested bit rate, frame target bit allocation and an as encoded bit rate.

Region of interest quality controllable video coding techniques

The systems and methods are configured to efficiently and effectively determine or find an estimated optimal encoding parameter set. In one embodiment, a video encoding parameter set estimation method comprises: performing an offline encoding parameter set characteristic prediction process that determines an estimate of a candidate encoding parameter set characteristic; and performing an encoding parameter set search process that identifies a predicted or estimated optimal video encoding parameter set. The encoding parameter set search process can include applying a constraint to the candidate encoding parameter set characteristic; and determining if candidate encoding parameter set meets an objective, wherein the determining is performed if the constraint is satisfied. The candidate encoding parameter set characteristic can be an estimated encoding time of the candidate encoding parameter set. The objective can be the best video quality out of a plurality of candidate encoding parameter sets.

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Hangzhou, China

Sicheng Li