[2307.02430] Base Layer Efficiency in Scalable Human-Machine Coding
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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2307.02430 (eess)
[Submitted on 5 Jul 2023]

Title:Base Layer Efficiency in Scalable Human-Machine Coding

Authors:Yalda Foroutan, Alon Harell, Anderson de Andrade, Ivan V. Bajić
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Abstract:A basic premise in scalable human-machine coding is that the base layer is intended for automated machine analysis and is therefore more compressible than the same content would be for human viewing. Use cases for such coding include video surveillance and traffic monitoring, where the majority of the content will never be seen by humans. Therefore, base layer efficiency is of paramount importance because the system would most frequently operate at the base-layer rate. In this paper, we analyze the coding efficiency of the base layer in a state-of-the-art scalable human-machine image codec, and show that it can be improved. In particular, we demonstrate that gains of 20-40% in BD-Rate compared to the currently best results on object detection and instance segmentation are possible.
Comments: 5 pages, 6 figures, IEEE ICIP 2023
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2307.02430 [eess.IV]
  (or arXiv:2307.02430v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2307.02430

Submission history

From: Ivan Bajic [view email]
[v1] Wed, 5 Jul 2023 16:52:06 UTC (917 KB)
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