PH12022552241A1 - Parallelized rate-distortion optimized quantization using deep learning - Google Patents
Parallelized rate-distortion optimized quantization using deep learningInfo
- Publication number
- PH12022552241A1 PH12022552241A1 PH1/2022/552241A PH12022552241A PH12022552241A1 PH 12022552241 A1 PH12022552241 A1 PH 12022552241A1 PH 12022552241 A PH12022552241 A PH 12022552241A PH 12022552241 A1 PH12022552241 A1 PH 12022552241A1
- Authority
- PH
- Philippines
- Prior art keywords
- block
- video encoder
- coefficients
- probabilities
- transform coefficient
- Prior art date
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- 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
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- G—PHYSICS
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- G06N3/00—Computing arrangements based on biological models
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- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- 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]
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- G06N3/00—Computing arrangements based on biological models
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- G06N3/047—Probabilistic or stochastic networks
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- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
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- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G06T9/00—Image coding
- G06T9/002—Image coding using neural networks
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- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
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- H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
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- H04N19/146—Data rate or code amount at the encoder output
- H04N19/147—Data rate or code amount at the encoder output according to rate distortion criteria
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- H04N19/157—Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
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- 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
- H04N19/176—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 block, e.g. a macroblock
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- 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
- H04N19/18—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 a set of transform coefficients
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- 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
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/48—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using compressed domain processing techniques other than decoding, e.g. modification of transform coefficients, variable length coding [VLC] data or run-length data
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- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
- H04N19/61—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding in combination with predictive coding
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- H—ELECTRICITY
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- H—ELECTRICITY
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- H04Q—SELECTING
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- H—ELECTRICITY
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S706/00—Data processing: artificial intelligence
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Automation & Control Theory (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
- Compression, Expansion, Code Conversion, And Decoders (AREA)
Applications Claiming Priority (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202063011685P | 2020-04-17 | 2020-04-17 | |
| US202063034618P | 2020-06-04 | 2020-06-04 | |
| US17/070,589 US12058348B2 (en) | 2020-04-17 | 2020-10-14 | Parallelized rate-distortion optimized quantization using deep learning |
| PCT/US2021/023680 WO2021211270A1 (en) | 2020-04-17 | 2021-03-23 | Parallelized rate-distortion optimized quantization using deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| PH12022552241A1 true PH12022552241A1 (en) | 2024-03-11 |
Family
ID=78082393
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PH1/2022/552241A PH12022552241A1 (en) | 2020-04-17 | 2021-03-23 | Parallelized rate-distortion optimized quantization using deep learning |
Country Status (9)
| Country | Link |
|---|---|
| US (1) | US12058348B2 (https=) |
| EP (1) | EP4136837A1 (https=) |
| JP (1) | JP7642671B2 (https=) |
| KR (1) | KR20230007313A (https=) |
| CN (1) | CN115336266B (https=) |
| BR (1) | BR112022020125A2 (https=) |
| PH (1) | PH12022552241A1 (https=) |
| TW (1) | TW202145792A (https=) |
| WO (1) | WO2021211270A1 (https=) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11490083B2 (en) | 2020-02-05 | 2022-11-01 | Qualcomm Incorporated | Learned low-complexity adaptive quantization for video compression |
| US20220215265A1 (en) * | 2021-01-04 | 2022-07-07 | Tencent America LLC | Method and apparatus for end-to-end task-oriented latent compression with deep reinforcement learning |
| US12244792B2 (en) * | 2021-03-30 | 2025-03-04 | Sony Interactive Entertainment Europe Limited | Processing image data |
| US11368349B1 (en) * | 2021-11-15 | 2022-06-21 | King Abdulaziz University | Convolutional neural networks based computationally efficient method for equalization in FBMC-OQAM system |
| JP7825447B2 (ja) * | 2022-02-14 | 2026-03-06 | 日本放送協会 | 符号化装置、プログラム、及びモデル生成方法 |
| WO2023169501A1 (en) * | 2022-03-09 | 2023-09-14 | Beijing Bytedance Network Technology Co., Ltd. | Method, apparatus, and medium for visual data processing |
| US20230306239A1 (en) * | 2022-03-25 | 2023-09-28 | Tencent America LLC | Online training-based encoder tuning in neural image compression |
| US20230316588A1 (en) * | 2022-03-29 | 2023-10-05 | Tencent America LLC | Online training-based encoder tuning with multi model selection in neural image compression |
| US12231183B2 (en) * | 2022-04-29 | 2025-02-18 | Qualcomm Incorporated | Machine learning for beam predictions with confidence indications |
| CN114708436B (zh) * | 2022-06-02 | 2022-09-02 | 深圳比特微电子科技有限公司 | 语义分割模型的训练方法、语义分割方法、装置和介质 |
| CN115209147B (zh) * | 2022-09-15 | 2022-12-27 | 深圳沛喆微电子有限公司 | 摄像头视频传输带宽优化方法、装置、设备及存储介质 |
| CN116366846B (zh) * | 2023-03-14 | 2025-11-11 | 北京百度网讯科技有限公司 | 视频编码方法、装置以及设备 |
| CN117764192A (zh) * | 2023-07-31 | 2024-03-26 | 中国银联股份有限公司 | 构建对比矩阵的方法和系统以及层次分析方法和系统 |
| WO2025114549A1 (en) * | 2023-12-01 | 2025-06-05 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Block-based codec supporting transform coefficient prediction and/or transform improvement |
| US20250307133A1 (en) * | 2024-03-28 | 2025-10-02 | Advanced Micro Devices, Inc. | Offloading Quantization of Directional Blocked Data Formats to Near-Memory Units |
| WO2025238505A1 (en) * | 2024-05-13 | 2025-11-20 | Imax Corporation | Large multimodal model-based video encoding optimization |
Family Cites Families (23)
| Publication number | Priority date | Publication date | Assignee | Title |
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| KR20030009575A (ko) * | 2001-06-26 | 2003-02-05 | 박광훈 | 신경망 분류기를 이용한 동영상 전송률 제어 장치 및 그방법 |
| US7620103B2 (en) | 2004-12-10 | 2009-11-17 | Lsi Corporation | Programmable quantization dead zone and threshold for standard-based H.264 and/or VC1 video encoding |
| US7889790B2 (en) | 2005-12-20 | 2011-02-15 | Sharp Laboratories Of America, Inc. | Method and apparatus for dynamically adjusting quantization offset values |
| US7995649B2 (en) | 2006-04-07 | 2011-08-09 | Microsoft Corporation | Quantization adjustment based on texture level |
| US8767834B2 (en) | 2007-03-09 | 2014-07-01 | Sharp Laboratories Of America, Inc. | Methods and systems for scalable-to-non-scalable bit-stream rewriting |
| ES2681209T3 (es) | 2009-09-10 | 2018-09-12 | Guangdong Oppo Mobile Telecommunications Corp., Ltd. | Técnicas de aceleración para una cuantificación optimizada de tasa de distorsión |
| US8170110B2 (en) * | 2009-10-16 | 2012-05-01 | Hong Kong Applied Science and Technology Research Institute Company Limited | Method and apparatus for zoom motion estimation |
| KR101492930B1 (ko) | 2010-09-14 | 2015-02-23 | 블랙베리 리미티드 | 변환 도메인 내의 어댑티브 필터링을 이용한 데이터 압축 방법 및 장치 |
| BR112013007023A2 (pt) * | 2010-09-28 | 2017-07-25 | Samsung Electronics Co Ltd | método de codificação de vídeo e método de decodificação de vídeo |
| US8553769B2 (en) * | 2011-01-19 | 2013-10-08 | Blackberry Limited | Method and device for improved multi-layer data compression |
| US9521410B2 (en) | 2012-04-26 | 2016-12-13 | Qualcomm Incorporated | Quantization parameter (QP) coding in video coding |
| US9213556B2 (en) * | 2012-07-30 | 2015-12-15 | Vmware, Inc. | Application directed user interface remoting using video encoding techniques |
| US9560386B2 (en) | 2013-02-21 | 2017-01-31 | Mozilla Corporation | Pyramid vector quantization for video coding |
| US9294766B2 (en) | 2013-09-09 | 2016-03-22 | Apple Inc. | Chroma quantization in video coding |
| US10057578B2 (en) * | 2014-10-07 | 2018-08-21 | Qualcomm Incorporated | QP derivation and offset for adaptive color transform in video coding |
| EP3545679B1 (en) * | 2016-12-02 | 2022-08-24 | Huawei Technologies Co., Ltd. | Apparatus and method for encoding an image |
| US10721471B2 (en) * | 2017-10-26 | 2020-07-21 | Intel Corporation | Deep learning based quantization parameter estimation for video encoding |
| KR102941657B1 (ko) * | 2018-02-08 | 2026-03-20 | 한국전자통신연구원 | 신경망에 기반하는 비디오 부호화 및 비디오 복호화를 위한 방법 및 장치 |
| EP3633990B1 (en) * | 2018-10-02 | 2021-10-27 | Nokia Technologies Oy | An apparatus and method for using a neural network in video coding |
| JP2020088740A (ja) * | 2018-11-29 | 2020-06-04 | ピクシブ株式会社 | 画像処理装置、画像処理方法及び画像処理プログラム |
| US12505580B2 (en) * | 2019-07-02 | 2025-12-23 | Telefonaktiebolaget Lm Ericsson (Publ) | Inference processing of data |
| US11496769B2 (en) * | 2019-09-27 | 2022-11-08 | Apple Inc. | Neural network based image set compression |
| CN112819699B (zh) * | 2019-11-15 | 2024-11-05 | 北京金山云网络技术有限公司 | 视频处理方法、装置及电子设备 |
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- 2020-10-14 US US17/070,589 patent/US12058348B2/en active Active
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2021
- 2021-03-23 KR KR1020227032350A patent/KR20230007313A/ko active Pending
- 2021-03-23 JP JP2022557846A patent/JP7642671B2/ja active Active
- 2021-03-23 PH PH1/2022/552241A patent/PH12022552241A1/en unknown
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- 2021-03-23 EP EP21718744.2A patent/EP4136837A1/en active Pending
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- 2021-03-23 WO PCT/US2021/023680 patent/WO2021211270A1/en not_active Ceased
- 2021-04-15 TW TW110113490A patent/TW202145792A/zh unknown
Also Published As
| Publication number | Publication date |
|---|---|
| US12058348B2 (en) | 2024-08-06 |
| JP2023522575A (ja) | 2023-05-31 |
| CN115336266B (zh) | 2025-09-23 |
| BR112022020125A2 (pt) | 2022-11-29 |
| WO2021211270A1 (en) | 2021-10-21 |
| KR20230007313A (ko) | 2023-01-12 |
| EP4136837A1 (en) | 2023-02-22 |
| JP7642671B2 (ja) | 2025-03-10 |
| TW202145792A (zh) | 2021-12-01 |
| CN115336266A (zh) | 2022-11-11 |
| US20210329267A1 (en) | 2021-10-21 |
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