Large-Scale Crowdsourcing Subjective Quality Evaluation of Learning-Based Image Coding
Author:
Affiliation:
1. Multimedia Signal Processing Group - Ecole Polytechnique Fédérale de Lausanne
2. Instituto de Telecornunicações - Instituto Superior Técnico
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9675283/9675313/09675314.pdf?arnumber=9675314
Reference21 articles.
1. Learning-based image coding: early solutions reviewing and subjective quality evaluation;ascenso;Optics Photonics and Digital Technologies for Imaging Applications IV,2020
2. QualityCrowd — A framework for crowd-based quality evaluation
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1. CPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression;2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC);2023-10-31
2. On the Performance of Subjective Visual Quality Assessment Protocols for Nearly Visually Lossless Image Compression;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26
3. JPEG AIC-3 Dataset: Towards Defining the High Quality to Nearly Visually Lossless Quality Range;2023 15th International Conference on Quality of Multimedia Experience (QoMEX);2023-06-20
4. Towards JPEG AIC part 3: visual quality assessment of high to visually-lossless image coding;Applications of Digital Image Processing XLV;2022-10-03
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