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João Ascenso

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Shima Mohammadi

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Publications

2024
[i10]
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  - journals/corr/abs-2403-11241
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  - https://dblp.org/rec/journals/corr/abs-2403-11241
Shima Mohammadi, Yaojun Wu, João Ascenso:
Fidelity-preserving Learning-Based Image Compression: Loss Function and Subjective Evaluation Methodology. CoRR abs/2403.11241 (2024)
2023
[c93]
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  - conf/mm/MohammadiA23
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Shima Mohammadi, João Ascenso:
Predictive Sampling for Efficient Pairwise Subjective Image Quality Assessment. ACM Multimedia 2023: 6676-6684
[c92]
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  - conf/mm/TestolinaLRMAPE23
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Michela Testolina, Davi Lazzarotto, Rafael Rodrigues, Shima Mohammadi, João Ascenso, António M. G. Pinheiro, Touradj Ebrahimi:
On the Performance of Subjective Visual Quality Assessment Protocols for Nearly Visually Lossless Image Compression. ACM Multimedia 2023: 6715-6723
[c90]
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  - conf/vcip/MohammadiWA23
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Shima Mohammadi, Yaojun Wu, João Ascenso:
Fidelity-preserving Learning-Based Image Compression: Loss Function and Subjective Evaluation Methodology. VCIP 2023: 1-5
[i8]
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  - journals/corr/abs-2311-03850
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Shima Mohammadi, João Ascenso:
Predictive Sampling for Efficient Pairwise Subjective Image Quality Assessment. CoRR abs/2311.03850 (2023)
[i7]
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  - journals/corr/abs-2311-06084
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Shima Mohammadi, João Ascenso:
Perceptual impact of the loss function on deep-learning image coding performance. CoRR abs/2311.06084 (2023)
[i6]
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  - journals/corr/abs-2311-06093
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Shima Mohammadi, João Ascenso:
Evaluation of Sampling Algorithms for a Pairwise Subjective Assessment Methodology. CoRR abs/2311.06093 (2023)
2022
[c88]
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  - conf/ism/MohammadiA22
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Shima Mohammadi, João Ascenso:
Evaluation of Sampling Algorithms for a Pairwise Subjective Assessment Methodology. ISM 2022: 288-292
[c87]
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  - conf/pcs/MohammadiA22
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Shima Mohammadi, João Ascenso:
Perceptual impact of the loss function on deep-learning image coding performance. PCS 2022: 37-41

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