Compressed Video Quality Assessment for Super-Resolution: A Benchmark and a Quality Metric

Evgeney, Bogatyrev,; Molodetskikh, Ivan; Vatolin, Dmitriy; Галактионов, В. А.

doi:10.20948/prepr-2022-81

Cited by 2 publications

(1 citation statement)

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“…This metric inherently provides local measures of quality in addition to global scores. As there are some research studies (Bogatyrev et al, 2023 ) that have proposed PSNR and SSIM do not sufficiently provide overall quality measures, we have also considered counting PIQE values in the experiments.…”

Section: Methodsmentioning

confidence: 99%

Super resolution-based methodology for self-supervised segmentation of microscopy images

Bommanapally,

Abeyrathna,

Chundi

et al. 2024

Front. Microbiol.

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Data-driven Artificial Intelligence (AI)/Machine learning (ML) image analysis approaches have gained a lot of momentum in analyzing microscopy images in bioengineering, biotechnology, and medicine. The success of these approaches crucially relies on the availability of high-quality microscopy images, which is often a challenge due to the diverse experimental conditions and modes under which these images are obtained. In this study, we propose the use of recent ML-based image super-resolution (SR) techniques for improving the image quality of microscopy images, incorporating them into multiple ML-based image analysis tasks, and describing a comprehensive study, investigating the impact of SR techniques on the segmentation of microscopy images. The impacts of four Generative Adversarial Network (GAN)- and transformer-based SR techniques on microscopy image quality are measured using three well-established quality metrics. These SR techniques are incorporated into multiple deep network pipelines using supervised, contrastive, and non-contrastive self-supervised methods to semantically segment microscopy images from multiple datasets. Our results show that the image quality of microscopy images has a direct influence on the ML model performance and that both supervised and self-supervised network pipelines using SR images perform better by 2%–6% in comparison to baselines, not using SR. Based on our experiments, we also establish that the image quality improvement threshold range [20–64] for the complemented Perception-based Image Quality Evaluator(PIQE) metric can be used as a pre-condition by domain experts to incorporate SR techniques to significantly improve segmentation performance. A plug-and-play software platform developed to integrate SR techniques with various deep networks using supervised and self-supervised learning methods is also presented.

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Section: Methodsmentioning

confidence: 99%