A no-reference perceptual image quality assessment database for learned image codecs
The drastic growth of research in image compression, especially deep learning-based image compression techniques, poses new challenges to objective image quality assessment (IQA). Typical artifacts encountered in the emerging image codecs are significantly different from that produced by traditional...
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| Veröffentlicht in: | Journal of visual communication and image representation Jg. 88; S. 103617 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Inc
01.10.2022
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| Schlagworte: | |
| ISSN: | 1047-3203, 1095-9076 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | The drastic growth of research in image compression, especially deep learning-based image compression techniques, poses new challenges to objective image quality assessment (IQA). Typical artifacts encountered in the emerging image codecs are significantly different from that produced by traditional block-based codecs, leading to inapplicability of the existing objective IQA algorithms. Towards advancing the development of objective IQA algorithms for recent compression artifacts, we built a learning-based compressed image quality assessment (LCIQA) database involving traditional block-based image codecs, hybrid neural network based image codecs, convolutional neural network based and generative adversarial network (GAN) based end-to-end optimized image coding approaches. Our study confirms the statistical difference and human perception difference between reconstructions of learned compression and traditional block-based compression. We propose a two-step deep learning model for learning-based compressed image quality assessment. Extensive experiments on LCIQA database demonstrate that our proposed model performs better than other counterparts on learning-based compressed images, especially on GAN compressed images, and achieves competitive performance to the state-of-the-art IQA metrics on traditional compressed images.
•An image quality database for learning-based compressed images is established.•Statistical difference and human perception difference between reconstructions of learned and block-based compression are proved.•Pre-training an image codec type identification network contributes to the quality–relevant features extraction for GAN compressed images. |
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| ISSN: | 1047-3203 1095-9076 |
| DOI: | 10.1016/j.jvcir.2022.103617 |