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Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics
Open accessAbstract
Video coding, which targets to compress and reconstruct the whole frame, and feature compression, which only preserves and transmits the most critical information, stand at two ends of the scale. That is, one is with compactness and efficiency to serve for machine vision, and the other is with full fidelity, bowing to human perception. The recent endeavors in imminent trends of video compression, e.g. deep learning based coding tools and end-to-end image/video coding, and MPEG-7 compact feature descriptor standards, i.e. Compact Descriptors for Visual Search and Compact Descriptors for Video Analysis, promote the sustainable and fast development in their own directions, respectively. In this paper, thanks to booming AI technology, e.g. prediction and generation models, we carry out exploration in the new area, Video Coding for Machines (VCM), arising from the emerging MPEG standardization efforts1. Towards collaborative compression and intelligent analytics, VCM attempts to bridge the gap between feature coding for machine vision and video coding for human vision. Aligning with the rising Analyze then Compress instance Digital Retina, the definition, formulation, and paradigm of VCM are given first. Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications. Finally, we come up with potential VCM solutions, and the preliminary results have demonstrated the performance and efficiency gains. Further direction is discussed as well.
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Self-citationVideo Imagination from a Single Image with Transformation Generation
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HNIP: Compact Deep Invariant Representations for Video Matching, Localization, and Retrieval
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Self-citationSpike Coding for Dynamic Vision Sensor in Intelligent Driving
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Self-citationJoint Feature and Texture Coding: Toward Smart Video Representation via Front-End Intelligence
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An Efficient Coding Method for Spike Camera Using Inter-Spike Intervals
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Self-citationCNN oriented fast HEVC intra CU mode decision
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Self-citationRate-adaptive Compact Fisher Codes for Mobile Visual Search
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Self-citationSelection of local features for visual search
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Efficient Multiple-Line-Based Intra Prediction for HEVC
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Keypoint Encoding for Improved Feature Extraction From Compressed Video at Low Bitrates
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Scalable Facial Image Compression with Deep Feature Reconstruction
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Self-citationRobust emotion recognition from low quality and low bit rate video: A deep learning approach
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Self-citationDeep Reference Generation With Multi-Domain Hierarchical Constraints for Inter Prediction
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Self-citationMemory-Efficient Image Databases for Mobile Visual Search
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Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding From Videos
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Self-citationToward Knowledge as a Service Over Networks: A Deep Learning Model Communication Paradigm
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Self-citationAn Emerging Coding Paradigm Vcm: A Scalable Coding Approach Beyond Feature And Signal
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Self-citationLou, Y., Duan, L., Wang, S., Chen, Z., Bai, Y., Chen, C. W., & Gao, W. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2916488 · Cited by 25
Self-citationA Group Variational Transformation Neural Network for Fractional Interpolation of Video Coding
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The Shape of Sight: A Homological Framework for Unifying Visual PerceptionPreprint
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A CNN-Based In-Loop Filter with CU Classification for HEVC
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Partition Tree Guided Progressive Rethinking Network for in-Loop Filtering of HEVC
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Self-citationImproved coding for image feature location information
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Jointly Embedding Multiple Single-Cell Omics Measurements
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Self-citationConvolutional Neural Network-Based Invertible Half-Pixel Interpolation Filter for Video Coding
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Practical Stacked Non-local Attention Modules for Image Compression
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Fast MPEG-CDVS Encoder With GPU-CPU Hybrid Computing
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Self-citationDigital retina: revolutionizing camera systems for the smart city
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Self-citationTowards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
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Self-citationDeep Scalable Image Compression via Hierarchical Feature Decorrelation
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Self-citationData-Driven Lightweight Interest Point Selection for Large-Scale Visual Search
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Self-citationCodebook-Free Compact Descriptor for Scalable Visual Search
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Self-citationCombined Spatial And Temporal Coding Of Digital Image Sequences
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Toward Intelligent Visual Sensing and Low-cost Analysis: A Collaborative Computing Approach
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Self-citationTowards Coding for Human and Machine Vision: A Scalable Image Coding ApproachPreprint
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Self-citation
Metadata
- Authors
- Duan, L., Liu, J., Yang, W., Huang, T., & Gao, W.
- Affiliations
- Peking University (CN)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2020
- Volume
- 29
- Pages
- 8680-8695
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W3082548248
- Cited by
- 260
- References
- 144
- Citations by year
- 2026: 25 · 2025: 43 · 2024: 59 · 2023: 51 · 2022: 38 · 2021: 37 · 2020: 7
- FWCI
- 18.00
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Keywords
- video coding for machines, feature compression, MPEG standardization, video compression, intelligent analytics
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