Video Coding for Machines: Partial transmission of SIFT features

Maćkowiak, Sławomir; Domański, Marek; Rożek, Sławomir; Cywiński, Dominik; Szkiełda, Jakub

doi:10.48550/arxiv.2201.02689

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Video Coding For Machines1

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2022

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“…The work [44] deals with Video Coding for Machines that is a new paradigm in video coding related to consumption of decoded video by humans and machines. For such tasks, joint transmission of compressed video and features is considered.…”

Section: Video Coding For Machinesmentioning

confidence: 99%

Task Oriented Video Coding: A Survey

Wood¹

2022

Preprint

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Video coding technology has been continuously improved for higher compression ratio with higher resolution. However, the state-of-the-art video coding standards, such as H.265/HEVC and Versatile Video Coding, are still designed with the assumption the compressed video will be watched by humans. With the tremendous advance and maturation of deep neural networks in solving computer vision tasks, more and more videos are directly analyzed by deep neural networks without humans' involvement. Such a conventional design for video coding standard is not optimal when the compressed video is used by computer vision applications. While the human visual system is consistently sensitive to the content with high contrast, the impact of pixels on computer vision algorithms is driven by specific computer vision tasks. In this paper, we explore and summarize recent progress on computer vision task oriented video coding and emerging video coding standard, Video Coding for Machines.

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Section: Video Coding For Machinesmentioning

confidence: 99%

Task Oriented Video Coding: A Survey

Wood¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

Video Coding for Machines: Partial transmission of SIFT features

Cited by 1 publication

References 0 publications

Task Oriented Video Coding: A Survey

Task Oriented Video Coding: A Survey

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