BR112022016793A2 - Compressão de vídeo usando sistemas de aprendizado de máquina de base recorrente - Google Patents
Compressão de vídeo usando sistemas de aprendizado de máquina de base recorrenteInfo
- Publication number
- BR112022016793A2 BR112022016793A2 BR112022016793A BR112022016793A BR112022016793A2 BR 112022016793 A2 BR112022016793 A2 BR 112022016793A2 BR 112022016793 A BR112022016793 A BR 112022016793A BR 112022016793 A BR112022016793 A BR 112022016793A BR 112022016793 A2 BR112022016793 A2 BR 112022016793A2
- Authority
- BR
- Brazil
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- neural network
- network system
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- Prior art date
Links
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
- H04N19/436—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation using parallelised computational arrangements
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
- G06N3/0455—Auto-encoder networks; Encoder-decoder networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0495—Quantised networks; Sparse networks; Compressed networks
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
- H04N19/136—Incoming video signal characteristics or properties
- H04N19/137—Motion inside a coding unit, e.g. average field, frame or block difference
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
- H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
- H04N19/172—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/46—Embedding additional information in the video signal during the compression process
- H04N19/463—Embedding additional information in the video signal during the compression process by compressing encoding parameters before transmission
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
- H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computing Systems (AREA)
- Signal Processing (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Image Analysis (AREA)
- Processing Or Creating Images (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
- Studio Devices (AREA)
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202062984673P | 2020-03-03 | 2020-03-03 | |
| US17/091,570 US11405626B2 (en) | 2020-03-03 | 2020-11-06 | Video compression using recurrent-based machine learning systems |
| PCT/US2021/013599 WO2021178050A1 (en) | 2020-03-03 | 2021-01-15 | Video compression using recurrent-based machine learning systems |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| BR112022016793A2 true BR112022016793A2 (pt) | 2022-10-11 |
Family
ID=77554929
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| BR112022016793A BR112022016793A2 (pt) | 2020-03-03 | 2021-01-15 | Compressão de vídeo usando sistemas de aprendizado de máquina de base recorrente |
Country Status (9)
| Country | Link |
|---|---|
| US (1) | US11405626B2 (enExample) |
| EP (1) | EP4115617A1 (enExample) |
| JP (1) | JP7628550B2 (enExample) |
| KR (1) | KR20220150298A (enExample) |
| CN (1) | CN115211115A (enExample) |
| BR (1) | BR112022016793A2 (enExample) |
| PH (1) | PH12022551821A1 (enExample) |
| TW (1) | TW202135529A (enExample) |
| WO (1) | WO2021178050A1 (enExample) |
Families Citing this family (49)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110677649B (zh) * | 2019-10-16 | 2021-09-28 | 腾讯科技(深圳)有限公司 | 基于机器学习的去伪影方法、去伪影模型训练方法及装置 |
| US12148120B2 (en) * | 2019-12-18 | 2024-11-19 | Ati Technologies Ulc | Frame reprojection for virtual reality and augmented reality |
| EP4144087A1 (en) | 2020-04-29 | 2023-03-08 | Deep Render Ltd | Image compression and decoding, video compression and decoding: methods and systems |
| US11425402B2 (en) * | 2020-07-20 | 2022-08-23 | Meta Platforms, Inc. | Cross-codec encoding optimizations for video transcoding |
| US11551090B2 (en) * | 2020-08-28 | 2023-01-10 | Alibaba Group Holding Limited | System and method for compressing images for remote processing |
| US20220148256A1 (en) * | 2020-11-11 | 2022-05-12 | Nvidia Corporation | Image blending using one or more neural networks |
| US20220151540A1 (en) * | 2020-11-19 | 2022-05-19 | 4N Inc. | Explainable artificial intelligence system for diagnosis of mental diseases and the control method thereof |
| WO2022128105A1 (en) * | 2020-12-17 | 2022-06-23 | Huawei Technologies Co., Ltd. | Decoding and encoding of neural-network-based bitstreams |
| CN116648906A (zh) * | 2020-12-24 | 2023-08-25 | 华为技术有限公司 | 通过指示特征图数据进行编码 |
| US11490078B2 (en) * | 2020-12-29 | 2022-11-01 | Tencent America LLC | Method and apparatus for deep neural network based inter-frame prediction in video coding |
| US11570465B2 (en) * | 2021-01-13 | 2023-01-31 | WaveOne Inc. | Machine-learned in-loop predictor for video compression |
| US11889057B2 (en) * | 2021-02-02 | 2024-01-30 | Novatek Microelectronics Corp. | Video encoding method and related video encoder |
| WO2022171841A2 (en) * | 2021-02-13 | 2022-08-18 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Encoder, decoder and methods for coding a picture using a convolutional neural network |
| US11399198B1 (en) * | 2021-03-01 | 2022-07-26 | Qualcomm Incorporated | Learned B-frame compression |
| US11831909B2 (en) * | 2021-03-11 | 2023-11-28 | Qualcomm Incorporated | Learned B-frame coding using P-frame coding system |
| WO2022195409A1 (en) * | 2021-03-18 | 2022-09-22 | Nokia Technologies Oy | Method, apparatus and computer program product for end-to-end learned predictive coding of media frames |
| WO2022221205A1 (en) | 2021-04-13 | 2022-10-20 | Headroom, Inc. | Video super-resolution using deep neural networks |
| US20230019874A1 (en) * | 2021-07-13 | 2023-01-19 | Nintendo Co., Ltd. | Systems and methods of neural network training |
| EP4420352A4 (en) * | 2021-10-18 | 2025-09-03 | Op Solutions Llc | SYSTEMS AND METHODS FOR OPTIMIZING A LOSS FUNCTION FOR VIDEO CODING FOR MACHINES |
| US11546614B1 (en) * | 2021-10-25 | 2023-01-03 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Encoder and decoder for encoding and decoding images |
| CN116112673A (zh) * | 2021-11-10 | 2023-05-12 | 华为技术有限公司 | 编解码方法及电子设备 |
| CN118216149A (zh) * | 2021-11-25 | 2024-06-18 | Oppo广东移动通信有限公司 | 解码方法、编码方法、解码器、编码器和编解码系统 |
| US20230214630A1 (en) * | 2021-12-30 | 2023-07-06 | Cron Ai Ltd. (Uk) | Convolutional neural network system, method for dynamically defining weights, and computer-implemented method thereof |
| CN118844064A (zh) * | 2022-01-21 | 2024-10-25 | 抖音视界有限公司 | 用于数据处理的方法、装置及介质 |
| US12548330B1 (en) | 2022-01-26 | 2026-02-10 | Upwork Inc. | Determining engagement using sensor data |
| DE112022006625T5 (de) * | 2022-02-08 | 2024-12-05 | Nvidia Corporation | Bilderzeugung unter verwendung eines neuronalen netzes |
| CN114545899B (zh) * | 2022-02-10 | 2024-09-10 | 上海交通大学 | 基于先验知识的燃气轮机系统多传感器故障信号重构方法 |
| US20230262237A1 (en) * | 2022-02-15 | 2023-08-17 | Adobe Inc. | System and methods for video analysis |
| WO2023167502A1 (ko) * | 2022-03-02 | 2023-09-07 | 엘지전자 주식회사 | 피쳐 부호화/복호화 방법, 장치, 비트스트림을 저장한 기록 매체 및 비트스트림 전송 방법 |
| KR20240160613A (ko) * | 2022-03-03 | 2024-11-11 | 두인 비전 컴퍼니 리미티드 | 시각 데이터를 처리하는 방법, 장치 및 매체 |
| CN115240099B (zh) * | 2022-06-21 | 2026-04-03 | 有米科技股份有限公司 | 基于多模态关联数据的模型训练方法及装置 |
| CN119452657A (zh) * | 2022-06-30 | 2025-02-14 | 交互数字Ce专利控股有限公司 | 在用于图像的深度编码系统中微调有限的参数集 |
| WO2024015638A2 (en) * | 2022-07-15 | 2024-01-18 | Bytedance Inc. | A neural network-based image and video compression method with conditional coding |
| WO2024020112A1 (en) * | 2022-07-19 | 2024-01-25 | Bytedance Inc. | A neural network-based adaptive image and video compression method with variable rate |
| CN115604475B (zh) * | 2022-08-12 | 2025-06-10 | 西安电子科技大学 | 一种多模态信源联合编码方法 |
| TWI832406B (zh) * | 2022-09-01 | 2024-02-11 | 國立陽明交通大學 | 反向傳播訓練方法和非暫態電腦可讀取媒體 |
| JP2025533398A (ja) | 2022-09-09 | 2025-10-07 | テスラ,インコーポレイテッド | 視覚に基づく占有率決定用の人工知能モデリング技術 |
| WO2024073080A1 (en) * | 2022-09-30 | 2024-04-04 | Tesla, Inc. | A file format for efficient storage and access of data |
| CN115294224B (zh) * | 2022-09-30 | 2022-12-16 | 南通市通州区华凯机械有限公司 | 用于驾驶模拟器的图像数据快速载入方法 |
| US20240169708A1 (en) * | 2022-11-10 | 2024-05-23 | Qualcomm Incorporated | Processing video data using delta quantization |
| TWI824861B (zh) * | 2022-11-30 | 2023-12-01 | 國立陽明交通大學 | 機器學習裝置及其訓練方法 |
| KR20240086085A (ko) * | 2022-12-09 | 2024-06-18 | 삼성전자주식회사 | 시맨틱 맵에 기초하여 프레임 이미지를 복원하는 방법 및 장치 |
| US12026924B1 (en) * | 2023-02-19 | 2024-07-02 | Deep Render Ltd. | Method and data processing system for lossy image or video encoding, transmission and decoding |
| WO2024175727A1 (en) * | 2023-02-22 | 2024-08-29 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Deep video coding with block-based motion estimation |
| CN116708796B (zh) * | 2023-06-21 | 2026-04-24 | 三星电子(中国)研发中心 | 视频中间帧的生成方法和装置 |
| TWI860054B (zh) * | 2023-08-22 | 2024-10-21 | 國立清華大學 | 訓練機器學習模型的方法、裝置和電腦程式產品 |
| WO2025198937A1 (en) * | 2024-03-16 | 2025-09-25 | Bytedance Inc. | Method, apparatus, and medium for visual data processing |
| CN119922332B (zh) * | 2025-01-21 | 2026-05-12 | 山东大学 | 一种基于隐式神经视频表示的视频编码方法及系统 |
| CN121053039B (zh) * | 2025-11-03 | 2026-01-27 | 北京铁力山科技股份有限公司 | 视频质量恢复方法、装置、设备及存储介质 |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10192327B1 (en) * | 2016-02-04 | 2019-01-29 | Google Llc | Image compression with recurrent neural networks |
| US10706351B2 (en) * | 2016-08-30 | 2020-07-07 | American Software Safety Reliability Company | Recurrent encoder and decoder |
| CN109451308B (zh) | 2018-11-29 | 2021-03-09 | 北京市商汤科技开发有限公司 | 视频压缩处理方法及装置、电子设备及存储介质 |
-
2020
- 2020-11-06 US US17/091,570 patent/US11405626B2/en active Active
-
2021
- 2021-01-15 KR KR1020227029923A patent/KR20220150298A/ko active Pending
- 2021-01-15 EP EP21703343.0A patent/EP4115617A1/en active Pending
- 2021-01-15 WO PCT/US2021/013599 patent/WO2021178050A1/en not_active Ceased
- 2021-01-15 JP JP2022551741A patent/JP7628550B2/ja active Active
- 2021-01-15 CN CN202180017106.0A patent/CN115211115A/zh active Pending
- 2021-01-15 PH PH1/2022/551821A patent/PH12022551821A1/en unknown
- 2021-01-15 BR BR112022016793A patent/BR112022016793A2/pt unknown
- 2021-01-15 TW TW110101726A patent/TW202135529A/zh unknown
Also Published As
| Publication number | Publication date |
|---|---|
| TW202135529A (zh) | 2021-09-16 |
| JP2023517846A (ja) | 2023-04-27 |
| WO2021178050A1 (en) | 2021-09-10 |
| US20210281867A1 (en) | 2021-09-09 |
| US11405626B2 (en) | 2022-08-02 |
| PH12022551821A1 (en) | 2024-02-12 |
| EP4115617A1 (en) | 2023-01-11 |
| KR20220150298A (ko) | 2022-11-10 |
| JP7628550B2 (ja) | 2025-02-10 |
| CN115211115A (zh) | 2022-10-18 |
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