@inproceedings{93f84c264be84073be5f43ab8151fb50,
title = "Feasibility Study of Multi-Layer VVC Coding Scheme for Hybrid Machine-Human Consumption",
abstract = "The proliferation of machine vision applications necessitates developing more efficient visual data compression schemes for machine consumption. However, numerous automated use cases still require keeping humans in the loop, leading to the need for a machine-optimized video streaming with the option for human supervision. This paper investigates the feasibility of using the multi-layer coding approach of the emerging Versatile Video Coding (VVC) standard to create favorable conditions for hybrid machine-human consumption. We introduce a multi-layer coding scheme, where the base layer (BL) is optimized for machines and the enhancement layer (EL) complements the stream for human vision. Our results demonstrate that the bitrate of the proposed multi-layer stream (BL + EL) is, on average, 11% higher than that of a single-layer VVC. However, the more compact BL yields overall bandwidth savings as long as the EL is required less than 80% of the time.",
keywords = "Video coding, Visualization, Machine vision, Bit rate, Data compression, Streaming media, Encoding, Region-of-interest (ROI), Versatile Video Coding (VVC), Multi-layer video coding, Video Coding for Machines (VCM), hybrid machine-human video consumption",
author = "Jaakko Laitinen and Tero Partanen and Alexandre Mercat and Jarno Vanne and Miska Hannuksela and Honglei Zhang and Alireza Aminlou and Francesco Cricri",
year = "2024",
month = jul,
doi = "10.1109/ICME57554.2024.10687938",
language = "English",
series = "IEEE International Conference on Multimedia and Expo",
publisher = "IEEE",
pages = "1--6",
booktitle = "2024 IEEE International Conference on Multimedia and Expo (ICME)",
address = "United States",
note = "IEEE International Conference on Multimedia and Expo ; Conference date: 15-07-2024 Through 19-07-2024",
}