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SMPTE Motion Imaging Journal ( Volume: 131, Issue: 6, July 2022)
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Analysis of Generation Loss for Wavelet-Based Compression

Metadata

Publisher
SMPTE — White Plains, NY, USA
Doc Type
Journal Article
Content Type
Original Research
Abbreviated Title
SMPTE Mot. Imag. J
Volume
131, No. 6, pp. 34–42
Abstract
This article investigates the sources of generation loss, the loss of picture quality over multiple coding generations, for waveletbased mezzanine video compression using the VC-2 video codec. The theoretical mechanisms of generation loss are presented, highlighting the detrimental interaction between quantization in the encoder and otherwise reversible processes in the decoder. A novel experimental investigation of the VC-2 video codec is presented, in which the significance of different sources of generation loss are compared and the parameters that impact the propagation of generation loss are contrasted. The most significant source of generation loss examined in this article is the addition of an “accuracy bit shift,” which improves subjective quality for a single coding generation but causes losses over subsequent generations. Of those investigated, the most significant way to mitigate the propagation of generation loss is to decrease the wavelet transform depth. This article concludes that, through careful parameter selection and signal processing design, generation loss for wavelet-based mezzanine codecs may be practically eliminated.
Publication Date
2022-07-01
DOI
10.5594/JMI.2022.3173625
ISSN
Print: 1545-0279 | Electronic: 2160-2492
Link
https://doi.org/10.5594/JMI.2022.3173625
Author(s)
Galen M. Reich
bio
Galen M. Reich received a BSc degree (Hons.) in physics and a PhD degree in electrical engineering for research into biologically inspired radar from the University of Birmingham, U.K., in 2015 and 2020, respectively. He is currently a research and development engineer with BBC Research and Development, London, U.K. His research interests include spatial audio, wavelet-based video compression, and explainable machine learning.
Jonathan Heathcote
bio
Jonathan Heathcote received a PhD degree in unconventional super-computer architectures for simulating biologically realistic neural networks from the University of Manchester, U.K., in 2016. He joined BBC Research and Development, Salford, U.K. in 2016, where he is currently a research engineer working on the verification of video codecs and private cloud architecture for broadcasters.
Keyword(s)
Broadcast, Error Analysis, Image Compression, Picture Quality, Video Compression, Wavelet Transform
Copyright
© 2022 SMPTE

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Galen M. Reich and Jonathan Heathcote; Analysis of Generation Loss for Wavelet-Based Compression, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 6, July 2022); SMPTE, 2022. Available at https://doi.org/10.5594/JMI.2022.3173625
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Galen M. Reich and Jonathan Heathcote; Analysis of Generation Loss for Wavelet-Based Compression, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 6, July 2022); SMPTE, 2022. Available at https://doi.org/10.5594/JMI.2022.3173625

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Galen M. Reich and Jonathan Heathcote; Analysis of Generation Loss for Wavelet-Based Compression, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 6, July 2022); SMPTE, 2022. Available at https://doi.org/10.5594/JMI.2022.3173625
Snippet:
<span class="citation">Galen M. Reich and Jonathan Heathcote; <cite>Analysis of Generation Loss for Wavelet-Based Compression</cite>, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 6, July 2022); SMPTE, 2022. Available at <a href="https://doi.org/10.5594/JMI.2022.3173625" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2022.3173625</a></span>

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Galen M. Reich and Jonathan Heathcote; Analysis of Generation Loss for Wavelet-Based Compression, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 6, July 2022); SMPTE, 2022
doi: 10.5594/JMI.2022.3173625
url: https://doi.org/10.5594/JMI.2022.3173625
Snippet:
<li>
Galen M. Reich and Jonathan Heathcote; <cite id="bib-10-5594-jmi-2022-3173625">Analysis of Generation Loss for Wavelet-Based Compression</cite>, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 6, July 2022); SMPTE, 2022
<span class="doi">10.5594/JMI.2022.3173625</span>
</li>