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...
Saved in:
| Published in: | Journal of visual communication and image representation Vol. 88; p. 103617 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Inc
01.10.2022
|
| Subjects: | |
| ISSN: | 1047-3203, 1095-9076 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1047-3203 1095-9076 |
| DOI: | 10.1016/j.jvcir.2022.103617 |