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Method and apparatus of neural networks with grouping for video coding

A neural network and video encoder technology, applied in biological neural network model, neural architecture, digital video signal modification and other directions, can solve problems such as large computational complexity

Active Publication Date: 2020-09-22
MEDIATEK INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, NN requires considerable computational complexity, and it is also expected to reduce the computational complexity of NN

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  • Method and apparatus of neural networks with grouping for video coding
  • Method and apparatus of neural networks with grouping for video coding
  • Method and apparatus of neural networks with grouping for video coding

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Embodiment Construction

[0032] The following description is of the best mode for carrying out the invention. The description has been made to illustrate the basic principles of the invention and not to be interpreted in a limiting sense. The scope of the invention is best determined by reference to the appended claims.

[0033] When NNs are applied to video codec systems, NNs can be applied to various signals along the signal processing path. image 3 An example of applying the NN 310 to the reconstructed signal is shown. exist image 3 , the input to NN 310 is the reconstructed pixels from REC 128. The output of the NN is the NN filtered reconstructed pixels, which can be further processed by a deblocking filter (ie, DF130). image 3 is an example of applying NN 310 in a video encoder, however, NN 310 can be applied in a corresponding video decoder in a similar way. CNN can be replaced by other NN variations such as DNN (Deep Fully Connected Feedforward Neural Network), RNN (Recurrent Neural Ne...

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Abstract

A method and apparatus of signal processing using a grouped neural network (NN) process are disclosed. A plurality of input signals for a current layer of NN process are grouped into multiple input groups comprising a first input group and a second input group. The neural network process for the current layer is partitioned into multiple NN processes comprising a first NN process and a second NN process. The first NN process and the second NN process are applied to the first input group and the second input group to generate a first output group and a second output group for the current layerof NN process respectively. In another method, the parameter set associated with a layer of NN process is coded using different code types.

Description

[0001] Related citations [0002] This application claims priority to US Provisional Patent Application No. 62 / 622,224, filed January 26, 2018, and US Provisional Patent Application No. 62 / 622,226, filed January 26, 2018. The US Provisional Patent Application is hereby incorporated by reference. technical field [0003] The present invention generally relates to Neural Networks. In particular, the present invention relates to reducing the complexity of neural network (NN) processing by dividing multiple inputs to a given layer of the neural network into multiple groups of inputs. Background technique [0004] A neural network (NN), also known as an "artificial" neural network (ANN), is an information processing system that has some of the same performance characteristics as a biological neural network. Neural network systems consist of many simple and highly interconnected processing components to process information through their dynamic responses to external inputs. The...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N19/14
CPCH04N19/439G06N3/045
Inventor 陈庆晔庄子德黄毓文柯扬
Owner MEDIATEK INC