The acquisition of depth information sensorial data is critically important in medical applications, such as the monitoring of the elderly or the extraction of human biometrics. In such applications, compressing the stream of depth video data plays an important role due to bandwidth constraints on transmission channels. This paper introduces a novel lightweight compression system that encodes the semantics of the input depth video and can operate in both lossless and L-infinite near-lossless compression modes. A quantization technique that targets the L-infinite norm for sparse distributions and a new L-infinite compression method that sets bounds on the quantization error is proposed. The proposed codec enables the control of the coding error on every pixel in the input video data, which is crucial in medical applications. Experimental results show an average improvement of 45\% and 17\% in lossless mode compared to standalone JPEG-LS and CALIC codecs, respectively. Furthermore, in near-lossless mode, the proposed codec achieves superior rate-distortion performance and reduced maximum error per frame compared to HEVC. Additionally, the proposed lightweight codec is designed to perform efficiently in real time when deployed on an embedded depth-camera platform.
Tahouri, MA, Alecu, AA, Denis, L & Munteanu, A 2025, 'Lossless and Near-Lossless L-Infinite Compression of Depth Video Data', Sensors, vol. 25, no. 5, 1403, pp. 1-15. https://doi.org/10.3390/s25051403
Tahouri, M. A., Alecu, A. A., Denis, L., & Munteanu, A. (2025). Lossless and Near-Lossless L-Infinite Compression of Depth Video Data. Sensors, 25(5), 1-15. Article 1403. https://doi.org/10.3390/s25051403
@article{d814bdc8def449ec83e0912eeab27172,
title = "Lossless and Near-Lossless L-Infinite Compression of Depth Video Data",
abstract = "The acquisition of depth information sensorial data is critically important in medical applications, such as the monitoring of the elderly or the extraction of human biometrics. In such applications, compressing the stream of depth video data plays an important role due to bandwidth constraints on transmission channels. This paper introduces a novel lightweight compression system that encodes the semantics of the input depth video and can operate in both lossless and L-infinite near-lossless compression modes. A quantization technique that targets the L-infinite norm for sparse distributions and a new L-infinite compression method that sets bounds on the quantization error is proposed. The proposed codec enables the control of the coding error on every pixel in the input video data, which is crucial in medical applications. Experimental results show an average improvement of 45\% and 17\% in lossless mode compared to standalone JPEG-LS and CALIC codecs, respectively. Furthermore, in near-lossless mode, the proposed codec achieves superior rate-distortion performance and reduced maximum error per frame compared to HEVC. Additionally, the proposed lightweight codec is designed to perform efficiently in real time when deployed on an embedded depth-camera platform.",
keywords = "depth map, lossless and near-lossless compression, foreground-background segmentation, L-infinite, quantization, MAXAD",
author = "Tahouri, \{Mohammad Ali\} and Alecu, \{Alin Adrian\} and Leon Denis and Adrian Munteanu",
note = "Funding Information: This work is funded by Innoviris Brussels, Belgium, in the research project MUSCLES, and by the National Program for Research of the National Association of Technical Universities, Romania, in the project GNAC ARUT 2023. Furthermore, Mintt S.A., Belgium, has provided the dataset used in this research. Publisher Copyright: {\textcopyright} 2025 by the authors.",
year = "2025",
month = feb,
day = "25",
doi = "10.3390/s25051403",
language = "English",
volume = "25",
pages = "1--15",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "5",
}