Large-Scale Crowdsourcing Subjective Quality Evaluation of Learning-Based Image Coding

Learning-based image codecs produce different compression artifacts, when compared to the blocking and blurring degradation introduced by conventional image codecs, such as JPEG, JPEG 2000 and HEIC. In this paper, a crowdsourcing based subjective quality evaluation procedure was used to benchmark a...

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Bibliographic Details
Published inVisual communications and image processing (Online) pp. 1 - 5
Main Authors Upenik, Evgeniy, Testolina, Michela, Ascenso, Joao, Pereira, Fernando, Ebrahimi, Touradj
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.12.2021
Subjects
Benchmark testing
Codecs
Crowdsourcing
deep learning
Degradation
Image coding
learning-based compression
subjective evaluation
Transform coding
Visual communication
visual quality
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Summary:Learning-based image codecs produce different compression artifacts, when compared to the blocking and blurring degradation introduced by conventional image codecs, such as JPEG, JPEG 2000 and HEIC. In this paper, a crowdsourcing based subjective quality evaluation procedure was used to benchmark a representative set of end-to-end deep learning-based image codecs submitted to the MMSP'2020 Grand Challenge on Learning-Based Image Coding and the JPEG AI Call for Evidence. For the first time, a double stimulus methodology with a continuous quality scale was applied to evaluate this type of image codecs. The subjective experiment is one of the largest ever reported including more than 240 pair-comparisons evaluated by 118 naïve subjects. The results of the benchmarking of learning-based image coding solutions against conventional codecs are organized in a dataset of differential mean opinion scores along with the stimuli and made publicly available.
ISSN:2642-9357
DOI:10.1109/VCIP53242.2021.9675314